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We present an end-to-end approach for trajectory clustering from aerial videos that enables the extraction of motion patterns in urban scenes. Camera motion is first compensated by mapping object trajectories on a reference plane. Then clustering is performed based on statistics from the Discrete Wavelet Transform coefficients extracted from the trajectories. Finally, motion patterns are identified by distance minimization from the centroids of the trajectory clusters. The experimental validation on four datasets shows the effectiveness of the proposed approach in extracting trajectory clusters. We also make available two new real-world aerial video datasets together with the estimated object trajectories and ground-truth cluster labeling.

We present a novel approach to the cluster labeling task using fusion methods. The core idea of our approach is to weigh labels, suggested by any labeler, according to the estimated labeler's decisiveness with respect to each of its suggested labels. We hypothesize that, a cluster labeler's labeling choice for a given cluster should remain stable even in the presence of a slightly incomplete cluster data. Using state-of-the-art cluster labeling and data fusion methods, evaluated over a large data collection of clusters, we demonstrate that, overall, the cluster labeling fusion methods that further consider the labeler's decisiveness provide the best labeling performance.

The clustering problem has been considered as one of the most important problems among those existing in the research area of unsupervised learning (a Machine Learning subarea). Although the development and improvement of algorithms that deal with this problem has been focused by many researchers, the main goal remains undefined: the understanding of generated clusters. As important as identifying clusters is to understand its meaning. A good cluster definition means a relevant understanding and can help the specialist to study or interpret data. Facing the problem of comprehend clusters - in other words, create labels - this paper presents a methodology to automatic labeling clusters based on techniques involving supervised and unsupervised learning plus a discretization model. Considering the problem from its inception, the problem of understanding clusters is dealt similar to a real problem, being initialized from clustering data. For this, an unsupervised learning technique is applied and then a supervised learning algorithm will detect which are the relevant attributes in order to define a specific cluster. Additionally, some strategies are used to create a methodology that presents a label (based on attributes and their values) for each cluster provided. Finally, this methodology is applied in four distinct databases presenting good results with an average above 88.79% of elements correctly labeled.

Undecagold cluster labeling of reactive cysteine residues in numerous proteins has allowed the labeled sites to be identified by electron microscopy, providing high-resolution information on the location and orientation of subunits in oligomeric enzymes, virus capsids, crystalline sheets of membrane proteins, and muscle thin filaments. The range of applications of undecagold cluster labeling has been greatly extended by the availability of site-directed mutagenesis to introduce cysteine residues at sites of interest. In this paper I discuss factors that can influence the extent and specificity of labeling, methods for biochemical analysis of undecagold-labeled proteins, and the effects of undecagold cluster labeling on biological activity.

Cluster algorithms have application in diverse areas, including statistical mechanics of polymer solutions, spin models in physics, and the study of ecological systems. Most parallel cluster labeling algorithms are designed for SIMD and MIMD multiprocessors and based on relaxation methods. We present a parallel 3-D cluster labeling algorithm based on mapping tables, for distributed memory environments. The proposed algorithm focuses on minimizing interprocess communication to enhance execution performance on workstation networks. We implemented the algorithm with the aid of theEcliPSeparallel replication toolkit, exploiting special tree-combining and data reduction features of the system. We report on performance results for experiments conducted on workstation clusters.

In this paper, two examples are given where the cluster labeling algorithm of Al-Futaisi and Patzek for arbitrary networks produces incorrect results. The reason for this is pointed out, and an improvement to the algorithm is proposed so that such errors can be avoided in future.

Support Vector Machines (SVMs) have been used in many areas such as regression, classification and novelity detection due to its accuracy and generalizability. Recently SVMs have been proposed for clustering analysis as well. Support Vector Clustering (SVC) works by finding the minimum enclosing sphere of data points using SVM training. SVC is a boundary based clustering method, where the support information is used to construct cluster boundaries. In support vector-based clustering algorithms, the main computational bottle-neck is the high cluster labeling time for each data point. In addition, in many cases labeled data is not available for use with SVC. This tends to restrict the scalability of the method and results in decreased efficiency. This also decreases the applicability of the SVC method to real-life datasets most of which do not have any class labels.. In this paper we present a technique that could be used to utilize SVM to improve the accuracy of clustering without the need of labeled dataset. We have used K-Means clustering algorithm to generate initial labels from the data and in the next step we have trained a Sequential Minimal Optimization (SMO) classifier on these labels. The original data set is then tested using the trained SMO classifier to improve classification accuracy. This process is continued iteratively and stops when further improvement is not possible. The proposed approach is compared against the popular Stephen winters-Hilt [1] approach and achieves 94% accuracy when applied to benchmark datasets.

International audience; Recent advances in miniaturization and automation of technologies have enabled cell-based assay high-throughput screening, bringing along new challenges in data analysis. Automation, standardization, reproducibility have become requirements for qualitative research. The Bioconductor community has worked in that direction proposing several R packages to handle high-throughput data including flow cytometry (FCM) experiment. Altogether, these packages cover the main steps of a FCM analysis workflow, that is, data management, quality assessment, normalization, outlier detection, automated gating, cluster labeling, and feature extraction. Additionally, the open-source philosophy of R and Bioconductor, which offers room for new development, continuously drives research and improvement of theses analysis methods, especially in the field of clustering and data mining. This review presents the principal FCM packages currently available in R and Bioconductor, their advantages and their limits.

To overcome the pricey computation required by redundant kernel function matrix and poor label performance, in a novel perspective, we present support vector clustering with boundary patterns (BPSVC for abbreviation) for efficiency. For the first phase, the conventional method of estimating the support vector function with the whole data is altered by only essential boundary patterns. Thence, BPSVC only need to solve a much simpler optimization problem. For the second phase of cluster labeling, both convex decomposition and cone cluster labeling method are employed by an ensemble labeling strategies for further improvements on accuracy and efficiency. Both theoretical analysis and experimental results show its superiorities in comparison of the state-of-the-art methods, especially for large-scale data analysis.

Self-organizing maps (SOMs) have been applied for practical data analysis, in the contexts of exploratory data analysis (EDA) and data mining (DM). Many SOM-based EDA and DM techniques require that descriptive labels be applied to a SOM’s neurons. Several techniques exist for labeling SOM neurons in a supervised fashion, using classification information associated with a set of labeling data examples. However, classification information is often unavailable, necessitating the use of unsupervised labeling approaches that do not require pre-classified labeling data. This paper surveys existing unsupervised neuron labeling techniques. A novel unsupervised labeling algorithm, namely unsupervised weight-based cluster labeling, is described and critically discussed. The proposed method labels emergent neuron clusters using sub-labels built from statistically significant weights. Visualizations of the labelings produced by a prototype of the proposed approach are presented.

The overwhelming amount of textual documents available nowadays highlights the need for information organization and discovery. Effectively organizing documents into a hierarchy of topics and subtopics makes it easier for users to browse the documents. This paper borrows community mining from social network analysis to generate a hierarchy of topically coherent document clusters. It focuses on giving the document clusters descriptive labels. We propose to use betweenness centrality measure in networks of co-occurring terms to label the clusters. We also incorporate keyphrase extraction and automatic titling in cluster labeling. The results show that the cluster labeling method utilizing KEA to extract keyphrases from the documents generates the best labels overall comparing to other methods and baselines.

In traditional pseudo feedback, the main reason of the topic drift is the low quality of the feedback source. Clustering search results is an effective way to improve the quality of feedback set. For XML data, how to effectively perform clustering algorithm and then identify good xml fragments from the clustering results is a intricate problem. This paper mainly focus on the latter problem. Based on k-mediod clustering results, This work firstly proposes an cluster label extraction method to select candidate relevant clusters. Secondly, multiple ranking features are introduced to assist the related xml fragments identification from the candidate clusters. Top N fragments compose the high quality pseudo feedback set finally. Experimental results on standard INEX test data show that in one hand, the proposed cluster label extraction method could obtain proper cluster key terms and lead to appropriate candidate cluster selection. On the other hand, the presented ranking features are beneficial to the relevant xml fragments identification. The quality of feedback set is ensured.

Accurate cloud detection in satellite data over land is a difficult task complicated by spatially and temporally varying land surface reflectivities and emissivities. The GOES split-and-merge clustering (GSMC) algorithm for cloud detection in GOES scenes over land provides a computationally efficient, scene specific way to circumvent these difficulties. The algorithm consists of three steps: 1) a split-and-merge clustering of the input data which segments the scene into its natural grouping; 2) a cluster labeling procedure which uses scene specific adaptive thresholds (as opposed to constant static thresholds) to label the clusters as either cloud or cloud-free land; and 3) a post-processing step which imposes a degree of spatial uniformity on the labeled land and cloud pixels. An “a priori” mask feature also enhances cloud detection in traditionally difficult scenes (e.g., clouds over bright desert). Results show that the GSMC algorithm is neither regionally nor temporally specific and can be used over a large range of solar altitudes.

Current approaches to improve the pattern recognition performance mainly focus on either extracting non-stationary and discriminant features of each class, or employing complex and nonlinear feature classifiers. However, little attention has been paid to the integration of these two approaches. Combining non-stationary feature analysis with complex feature classifiers, this article presents a novel direction to enhance the discriminatory power of pattern recognition methods. This approach, which is based on a fusion of non-stationary feature analysis with clustering techniques, proposes an algorithm to adaptively identify the feature vectors according to their importance in representing the patterns of discrimination. Non-stationary feature vectors are extracted using a non-stationary method based on time–frequency distribution and non-negative matrix factorization. The clustering algorithms including the K-means and self-organizing tree maps are utilized as unsupervised clustering methods followed by a supervised labeling. Two labeling methods are introduced: hard and fuzzy labeling. The article covers in detail the formulation of the proposed discriminant feature clustering method. Experiments performed with pathological speech classification, T-wave alternans evaluation from the surface electrocardiogram, audio scene analysis, and telemonitoring of Parkinson’s disease problems produced desirable results. The outcome demonstrates the benefits of non-stationary feature fusion with clustering methods for complex data analysis where existing approaches do not exhibit a high performance.

In this work, we will discuss how to improve Wikilabel, an approach which makes use of titles in Wikipedia pages to generate labels for documents, by retooling ideas from story link detection (SLD). A comparison of our approach against Elastic Net, a powerful machine learner, on the real world data, finds the visible superiority of our approach over the latter.

We propose a competence partitioning strategy for Web search result presentation: the unmodified head of a ranked result list is combined with a clustering of documents from the result list tail. We identify two principles to which such a clustering must adhere to improve the user's search experience: (1) Avoid the unwanted effect of query aspect repetition, which is called shadowing here. (2) Avoid extreme clusterings, i.e., neither the number of cluster labels nor the number of documents per cluster should exceed the size of the result list head. We present measures to quantify the shadowing effect, and with Faceted Clustering we introduce an algorithm that optimizes the identified principles. The key idea of Faceted Clustering is a dynamic, user-controlled reorganization of a clustering, similar to a faceted navigation system. We report on evaluations using the AMBIENT corpus and demonstrate the potential of our approach by a comparison with two well-known clustering search engines.

El clustering de documentos es un campo de investigación popular en los ámbitos del Procesamiento del Lenguaje Natural, la Minería de Datos y la Recuperación de información (RI). El problema de agrupar unidades léxicas mediante clustering ha sido menos estudiado y menos aún, el problema de etiquetar los clusters. Sin embargo, en nuestra aplicación que trata sobre la extracción de tuplas de relaciones para ser usadas como entrada a programas para dibujar diagramas de bloques o mapas conceptuales, este problema es fundamental. La valoración de varias estrategias de etiquetado de clusters de documentos nos revela que algunas de estas técnicas pueden ser también aplicadas para etiquetar nuestros clusters, compuestos por verbos semánticamente similares. Para confirmar esta suposición, llevamos a cabo una serie de experimentos y evaluamos su rendimiento contra baselines y un goldstandard de clusters etiquetados. Document clustering is a popular research field in Natural Language Processing, Data Mining and Information Retrieval. The problem of lexical unit (LU) clustering has been less addressed, and even less so the problem of labeling LU clusters. However, in our application that deals with the distillation of relational tuples from patent claims as input to block diagram or a concept map drawing programs, this problem is central. The assessment of various document cluster labeling techniques lets us assume that despite some significant differences that need to be taken into account some of these techniques may also be applied to verbal relation cluster labeling we are concerned with. To confirm this assumption, we carry out a number of experiments and evaluate their outcome against baselines and gold standard labeled clusters.

Clustering is of great practical value in retrieving reusable requirements artifacts from the ever-growing software project repositories. Despite the development of automated cluster labeling techniques in information retrieval, little is understood about automatic labeling of requirements clusters. In this paper, we review the literature on cluster labeling, and conduct an experiment to evaluate how automated methods perform in labeling requirements clusters. The results show that differential labeling outperforms cluster-internal labeling, and that hybrid method does not necessarily lead to the labels best matching human judgment. Our work sheds light on improving automated ways to support search-driven development.

Scatter/Gather systems are increasingly becoming useful in browsing document corpora. Usability of the present-day systems are restricted to monolingual corpora, and their methods for clustering and labeling do not easily extend to the multilingual setting, especially in the absence of dictionaries/machine translation. In this paper, we study the cluster labeling problem for multilingual corpora in the absence of machine translation, but using comparable corpora. Using a variational approach, we show that multilingual topic models can effectively handle the cluster labeling problem, which in turn allows us to design a novel Scatter/Gather system ShoBha. Experimental results on three datasets, namely the Canadian Hansards corpus, the entire overlapping Wikipedia of English, Hindi and Bengali articles, and a trilingual news corpus containing 41,000 articles, confirm the utility of the proposed system.

The paper addresses the missing user acceptance of web search result clustering. We report on selected analyses and propose new concepts to improve existing result clustering approaches. Our findings in a nutshell are: 1. Don't compete with a search engine's top hits. In response to a query we presume search engines to return an optimal result list in the sense of the probabilistic ranking principle: documents that are expected by the majority of users are placed on top and form the result list head. We argue that, with respect to the top results, it is not beneficial to replace this established form of result presentation. 2. Improve document access in the result list tail. Documents that address the information need of "minorities" appear at some position in the result list tail. Especially for ambiguous and multi-faceted queries we expect this tail to be long, with many users appreciating different documents. In this situation web search result clustering can improve user satisfaction by reorganizing the long tail into topic-specific clusters. 3. Avoid shadowing when constructing cluster labels. We show that most of the cluster labels that are generated by current clustering technology occur within the snippets of the result list head--an effect which we call shadowing. The value of such labels for topic organization and navigating within a clustering of the entire result list is limited. We propose and analyze a filtering approach to significantly alleviate the label shadowing effect.

This paper presents a particular approach to collective labeling of multiple documents, which works by associating the documents with Wikipedia pages and labeling them with headings the pages carry. The approach has an obvious advantage over past approaches in that it is able to produce fluent labels, as they are hand-written by human editors. We carried out some experiments on the TDT5 dataset, which found that the approach works rather robustly for an arbitrary set of documents in the news domain. Comparisons were made with some baselines, including the state of the art, with results strongly in favor of our approach.

A simple and intuitive way to organize a huge document collection is by a topic hierarchy. Generally two steps are carried out to build a topic hierarchy automatically: 1) hierarchical document clustering and 2) cluster labeling. For both steps, a good textual document representation is essential. The bag-of-words is the common way to represent text collections. In this representation, each document is represented by a vector where each word in the document collection represents a dimension (feature). This approach has well known problems as the high dimensionality and sparsity of data. Besides, most of the concepts are composed by more than one word, as "document engineering" or "text mining". In this paper an approach called bag-of-related-words is proposed to generate features compounded by a set of related words with a dimensionality smaller than the bag-of-words. The features are extracted from each textual document of a collection using association rules. Different ways to map the document into transactions in order to allow the extraction of association rules and interest measures to prune the number of features are analyzed. To evaluate how much the proposed approach can aid the topic hierarchy building, we carried out an objective evaluation for the clustering structure, and a subjective evaluation for topic hierarchies. All the results were compared with the bag-of-words. The obtained results demonstrated that the proposed representation is better than the bag-of-words for the topic hierarchy building.

International audience; Neural clustering algorithms show high performance in the general context of the analysis of homogeneous textual dataset. This is especially true for the recent adaptive versions of these algorithms, like the incremental growing neural gas algorithm (IGNG) and the labeling maximization based incremental growing neural gas algorithm (IGNG-F). In this paper we highlight that there is a drastic decrease of performance of these algorithms, as well as the one of more classical algorithms, when a heterogeneous textual dataset is considered as an input. Specific quality measures and cluster labeling techniques that are independent of the clustering method are used for the precise performance evaluation. We provide new variations to incremental growing neural gas algorithm exploiting in an incremental way knowledge from clusters about their current labeling along with cluster distance measure data. This solution leads to significant gain in performance for all types of datasets, especially for the clustering of complex heterogeneous textual data.

Cluster identification tasks occur in a multitude of contexts in physics and engineering such as, for instance, cluster algorithms for simulating spin models, percolation simulations, segmentation problems in image processing, or network analysis. While it has been shown that graphics processing units (GPUs) can result in speedups of two to three orders of magnitude as compared to serial codes on CPUs for the case of local and thus naturally parallelized problems such as single-spin flip update simulations of spin models, the situation is considerably more complicated for the nonlocal problem of cluster or connected component identification. I discuss the suitability of different approaches of parallelization of cluster labeling and cluster update algorithms for calculations on GPU and compare to the performance of serial implementations.

Neural clustering algorithms show high performance in the general context of the analysis of homogeneous textual dataset. This is especially true for the recent adaptive versions of these algorithms, like the incremental growing neural gas algorithm (IGNG) and the label maximization based incremental growing neural gas algorithm (IGNG-F). In this paper we highlight that there is a drastic decrease of performance of these algorithms, as well as the one of more classical algorithms, when a heterogeneous textual dataset is considered as an input. Specific quality measures and cluster labeling techniques that are independent of the clustering method are used for the precise performance evaluation. We provide variations to incremental growing neural gas algorithm exploiting in an incremental way knowledge from clusters about their current labeling along with cluster distance measure data. This solution leads to significant gain in performance for all types of datasets, especially for the clustering of complex heterogeneous textual data.

Conference name: 7th Asia Information Retrieval Societies Conference, AIRS 2011 Date of Conference: December 18-20, 2011 Search engines present query results as a long ordered list of web snippets divided into several pages. Post-processing of retrieval results for easier access of desired information is an important research problem. In this paper, we present a novel search result clustering approach to split the long list of documents returned by search engines into meaningfully grouped and labeled clusters. Our method emphasizes clustering quality by using cover coefficient-based and sequential k-means clustering algorithms. A cluster labeling method based on term weighting is also introduced for reflecting cluster contents. In addition, we present a new metric that employs precision and recall to assess the success of cluster labeling. We adopt a comparative strategy to derive the relative performance of the proposed method with respect to two prominent search result clustering methods: Suffix Tree Clustering and Lingo. Experimental results in the publicly available AMBIENT and ODP-239 datasets show that our method can successfully achieve both clustering and labeling tasks. © 2011 Springer-Verlag Berlin Heidelberg.

Ankara : The Department of Computer Engineering and the Graduate School of Engineering and Science of Bilkent University, 2011. Thesis (Master's) -- Bilkent University, 2011. Includes bibliographical references leaves 58-62. Search engines present query results as a long ordered list of web snippets divided into several pages. Post-processing of information retrieval results for easier access to the desired information is an important research problem. A post-processing technique is clustering search results by topics and labeling these groups to reflect the topic of each cluster. In this thesis, we present a novel search result clustering approach to split the long list of documents returned by search engines into meaningfully grouped and labeled clusters. Our method emphasizes clustering quality by using cover coefficient and sequential k-means clustering algorithms. Cluster labeling is crucial because meaningless or confusing labels may mislead users to check wrong clusters for the query and lose extra time. Additionally, labels should reflect the contents of documents within the cluster accurately. To be able to label clusters effectively, a new cluster labeling method based on term weighting is introduced. We also present a new metric that employs precision and recall to assess the success of cluster labeling. We adopt a comparative evaluation strategy to derive the relative performance of the proposed method with respect to the two prominent search result clustering methods: Suffix Tree Clustering and Lingo. Moreover, we perform the experiments using the publicly available Ambient and ODP-239 datasets. Experimental results show that the proposed method can successfully achieve both clustering and labeling tasks. Türel, Anıl M.S.

Cluster algorithms are non-local Monte Carlo update schemes which can greatly increase the efficiency of computer simulations of spin models of magnets. The major computational task in these algorithms is connected component labeling, to identify clusters of connected sites on a lattice. We have devised some new SIMD component labeling algorithms, and implemented them on the Connection Machine. We investigate their performance when applied to the cluster update of the two-dimensional Ising spin model. These algorithms could also be applied to other problems which use connected component labeling, such as percolation and image analysis.

Targeting the same objective of alleviating the manual work as automatic annotation, in this paper, we propose a novel framework with minimal human effort to manually annotate a large-scale image corpus. In this framework, a dynamic multi-scale cluster labeling strategy is proposed to manually label the clusters of similar image regions. The users label the multi-scale clusters of regions instead of individual images, thus each labeling operation can annotate hundreds or even thousands of images simultaneously with much reduced manual work. Meanwhile the manual labeling guarantees the accuracy of the labels. Compared to automatic annotation, the proposed framework is more flexible, general and effective, especially for annotating those labels with large semantic gaps. Experiments on NUS-WIDE dataset demonstrate that the proposed fast manual annotation framework is much more effective than automatic annotation and comparatively efficient.

This paper compares four commonly used fuzzy analytical methods for remote sensing digital image classification, i.e. fuzzy c-means, semi-supervised fuzzy cluster labeling, fuzzy nearest neighbor, and object-oriented fuzzy classifiers. Merits and weak points of each method were examined through a case study with a multispectral high-resolution airborne digital image of urban settings. Results showed that the fuzzy labeling approach produced the highest quality, which was followed by the object-oriented fuzzy classifier. As the former combines merits of supervised and unsupervised classifications, the latter takes the full account of contextual and spatial features.

A multiple-perspective co-citation analysis method is introduced for characterizing and interpreting the structure and dynamics of co-citation clusters. The method facilitates analytic and sense making tasks by integrating network visualization, spectral clustering, automatic cluster labeling, and text summarization. Co-citation networks are decomposed into co-citation clusters. The interpretation of these clusters is augmented by automatic cluster labeling and summarization. The method focuses on the interrelations between a co-citation cluster's members and their citers. The generic method is applied to a three-part analysis of the field of Information Science as defined by 12 journals published between 1996 and 2008: 1) a comparative author co-citation analysis (ACA), 2) a progressive ACA of a time series of co-citation networks, and 3) a progressive document co-citation analysis (DCA). Results show that the multiple-perspective method increases the interpretability and accountability of both ACA and DCA networks., Comment: 33 pages, 11 figures, 10 tables. To appear in the Journal of the American Society for Information Science and Technology

Document clustering is a powerful technique that has been widely used for organizing data into smaller and manageable information kernels. Several approaches have been proposed suffering however from problems like synonymy, ambiguity and lack of a descriptive content marking of the generated clusters. We are proposing the enhancement of standard kmeans algorithm using the external knowledge from WordNet hypernyms in a twofold manner: enriching the "bag of words" used prior to the clustering process and assisting the label generation procedure following it. Our experimentation revealed a significant improvement over standard kmeans for a corpus of news articles derived from major news portals. Moreover, the cluster labeling process generates useful and of high quality cluster tags.

Digital libraries allow organizing, classifying and publishing collections of electronic contents that are available in computers or networks. Also, digital libraries are easy to use and configure and they offer a user interface with access to fast searching and browsing over a repository of documents using a graphical interface. This article presents a digital library prototype for retrieving, indexing and clustering documents published on a website. The website may include unstructured, semi-structured and structured documents such as: web pages, scientific papers, news and documents in several formats that contain essentially text. The proposed prototype includes a clustering process that uses a conceptual algorithm and an a priori process of cluster labeling. Preliminary results correspond to tests made with different sets of documents published in a real website.

We present an algorithm for cluster dynamics to efficiently simulate large systems on MIMD parallel computers with large numbers of processors. The method divides physical space into rectangular cells which are assigned to processors and combines a serial local procedure with a nearest neighbor relaxation process. By controlling overhead and reducing inter-processor communication this method attains good performance and speed-up. The complexity and scaling properties of the algorithm are analyzed. The algorithm has been used to simulate large 2d Ising systems (up to 27808 X 27808 sites) with Swendsen-Wang dynamics. Typical updating times on the order of 82 nanosecs/site and efficiencies larger than 90% have been obtained using 256 processors on the CM-5 supercomputer., 16 pages (TeX macros included)

We introduce a simple multi-scale algorithm for connected component labeling on parallel computers, which we apply to the problem of labeling clusters in spin and percolation models. We give numerical evidence that it is only logarithmically slowed down in the critical limit. We also discuss, in light of the proposed Teraflop computers optimized for lattice gauge theories and other lattice problems, the minimum requirements for computer switchboard architectures for which one can efficiently implement such multi-scale algorithms.

This work investigates cluster labeling enhancement by utilizing Wikipedia, the free on-line encyclopedia. We describe a general framework for cluster labeling that extracts candidate labels from Wikipedia in addition to important terms that are extracted directly from the text. The "labeling quality" of each candidate is then evaluated by several independent judges and the top evaluated candidates are recommended for labeling. Our experimental results reveal that the Wikipedia labels agree with manual labels associated by humans to a cluster, much more than with significant terms that are extracted directly from the text. We show that in most cases even when human's associated label appears in the text, pure statistical methods have difficulty in identifying them as good descriptors. Furthermore, our experiments show that for more than 85% of the clusters in our test collection, the manual label (or an inflection, or a synonym of it) appears in the top five labels recommended by our system.

Results clustering in Web Searching is useful for providing users with overviews of the results and thus allowing them to restrict their focus to the desired parts. However, the task of deriving single-word or multiple-word names for the clusters (usually referred as cluster labeling ) is difficult, because they have to be syntactically correct and predictive. Moreover efficiency is an important requirement since results clustering is an online task. Suffix Tree Clustering (STC) is a clustering technique where search results (mainly snippets) can be clustered fast (in linear time), incrementally, and each cluster is labeled with a phrase. In this paper we introduce: (a) a variation of the STC, called STC+, with a scoring formula that favors phrases that occur in document titles and differs in the way base clusters are merged, and (b) a novel non merging algorithm called NM-STC that results in hierarchically organized clusters. The comparative user evaluation showed that both STC+ and NM-STC are significantly more preferred than STC, and that NM-STC is about two times faster than STC and STC+.

Support vector clustering (SVC) is an appealing approach that can detect cluster boundaries. In spite of its popularization in applications, it sees the critical bottleneck in cluster labeling. This paper presents a Novel Support Vector Clustering algorithm (NSVC) to go a further step in clustering labeling. NSVC consists of three phases: extract Data Representatives (DRs); cluster DRs; label non-DR data. The objective of traditional SVC is used by NSVC for finding DRs, but the Kernel scale of the objective is modified. DRs are grouped by Spectrum Analysis (SA) method, which simultaneously develops an informative metric. Non-DR data are labeled by a weighted kNN procedure that works in query’s neighborhood, which is formulated with the new metric and then enriched by the convex hull skill. Experiments on real datasets demonstrate the improvement of NSVC over its peers and the competitive performance with the state of the arts.

A new parallel cluster-finding algorithm is formulated by using multigrid relaxation methods very similar to those used for differential equation solvers. For percolation clusters, this approach drastically reduces critical slowing down relative to local or scan relaxation methods. Numerical studies of scaling properties with system size are presented in the case of the 2D percolation clusters of the Swendsen-Wang Ising dynamics running on the Connection Machine.

In this chapter, the applications of rough set theory (RST) in text mining are discussed and a new concept named “Rough Text” is presented along with some RST-based measures for the evaluation of decision systems. We will focus on the application of such concept in clustering validity, specifically cluster labeling and multi-document summarization. The experimental studies show that the proposed measures outperform several internal measures existing on literature. Additionally, the application of Rough Text is illustrated.

As an important boundary-based clustering algorithm, support vector clustering (SVC) can benefit many real applications owing to its capability of handling arbitrary cluster shapes, especially those directly or indirectly related to pattern exploration and description. As the application deepens, the importance of performance (i.e. criterions of accuracy and efficiency) of SVC increases. To identify gaps in the current methods and propose novel research directions for SVC, we present a survey of the literature in this area. Our approach is to classify the most recent advances into either theory or application. For theoretical contributions, advances related to parameter selection and optimization, dual-problem solutions, and cluster labeling are introduced. We also simultaneously summarize the advantages and drawbacks of each study. With respect to applications, we clearly describe eight groups of schemes based on SVC, either as individual or hybrid methods. Finally, we identify the gaps in SVC research and suggest several future research issues and trends.

In this paper we present a novel approach for labeling clusters of multimedia content that leverages supervised classification techniques in conjunction with unsupervised clustering. Recent research has produced significant results for automatic tagging of video content such as broadcast news. For example, powerful techniques have been demonstrated in the context of the NIST TRECVID video retrieval benchmark [1]. However, the information needs of users typically span a range of semantic concepts. One of the challenges of these multimedia retrieval systems is to organize the video data in such a way that allows the user to most efficiently navigate the semantic space for the video data set. One important tool for video data organization is clustering. However, clustering results cannot be leveraged effectively when they are not labeled. We propose to build on clustering by aggregating the automatically tagged semantics. We propose and compare four techniques for labeling the clusters and evaluate the performance compared to human labeled ground-truth. We present examples of the cluster labeling results obtained on the BBC stock shots from the TRECVID-2005 video data set.

In this demo we present a novel approach for labeling clusters in minimally annotated data archives. We propose to build on clustering by aggregating the automatically tagged semantics. We propose and compare four techniques for labeling the clusters and evaluate the performance compared to human labeled ground-truth. We define the error measures to quantify the results, and present examples of the cluster labeling results obtained on the BBC stock shots and broadcast news videos from the TRECVID-2005 video data set.

We describe ASK-GraphView, a node-link-based graph visualization system that allows clustering and interactive navigation of large graphs, ranging in size up to 16 million edges. The system uses a scalable architecture and a series of increasingly sophisticated clustering algorithms to construct a hierarchy on an arbitrary, weighted undirected input graph. By lowering the interactivity requirements we can scale to substantially bigger graphs. The user is allowed to navigate this hierarchy in a top down manner by interactively expanding individual clusters. ASK-GraphView also provides facilities for filtering and coloring, annotation and cluster labeling.

The support vector clustering (SVC) algorithm was introduced to get the number of the pinnacles in the result of the time-frequency analysis and Radon transform of the multi-component linear FM (LFM) signal, and to finish the detection of the components of the LFM signal. Meanwhile, the preprocessing to reduce the points' number of the input data-set for SVC, was proposed to improve the computation efficiency. And a novel cluster labeling method was developed to improve the SVC algorithm. The simulation results depict that the SVC-Radon-time-frequency approach is efficient for the detection and parameter estimation of the multi-component LFM signal with low SNR