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In order to avoid the problem of over-dependent on the similarity to select the band but ignoring the amount of information and the correlation of the band, this paper proposes a method of hyperspectral image feature perception based on clustering and intra-class frequency band index. In this paper, the band texture feature vectors are used to calculate the similarity matrix. Then affine propagation clustering algorithm clusters all the bands according to the similarity matrix. The band with the largest intra-class band index in a certain cluster is selected as the representative band, so as to achieve the purpose of band selection. Finally, support vector machine classification algorithm is used to classify the objects on the image after band selection. By combining the Affine Propagation algorithm and Intra-Class Band Index, this paper proposed the AP-ICBI algorithm so that the band with large amount of information and small correlation can be selected in the high-quality band clustering results. In the experiment, the overall classification accuracy (OA), the Kappa coefficient and user accuracy (UA) are taken as the evaluation indexes. The experimental results showed that the proposed AP-ICBI algorithm can effectively improve the classification accuracy comparing with other methods. [ABSTRACT FROM AUTHOR]

The paper presents different clustering approaches in legal judgments from the Special Civil Court located at the Federal University of Santa Catarina (JEC/UFSC). The subject is Consumer Law, specifically cases in which consumers claim moral and material compensation from airlines for service failures. To identify patterns from the dataset, we apply four types of clustering algorithms: Hierarchical and Lingo (soft clustering), K-means and Affinity Propagation (hard clustering). We evaluate the results based on the following criteria: (1) entropy and purity; (2) algorithm's ability in providing labels; (3) legal expert's evaluation; and (4) experimental complexity. The results demonstrate that the most advantageous approach is Hierarchical Clustering, since it has the best entropy and purity numbers, as well as the least difficulty for the expert to analyze the clusters, and the least experimental complexity. The main contribution of the paper is to show the advantages and disadvantages of each approach, especially to identify labels in unstructured and non-indexed legal texts. [ABSTRACT FROM AUTHOR]

This paper addresses the clustering of data in the hyperdimensional computing (HDC) domain. In prior work, an HDC-based clustering framework, referred to as HDCluster, has been proposed. However, the performance of the existing HDCluster is not robust. The performance of HDCluster is degraded as the hypervectors for the clusters are chosen at random during the initialization step. To overcome this bottleneck, we assign the initial cluster hypervectors by exploring the similarity of the encoded data, referred to as query hypervectors. Intra-cluster hypervectors have a higher similarity than inter-cluster hypervectors. Harnessing the similarity results among query hypervectors, this paper proposes four HDC-based clustering algorithms: similarity-based k-means, equal bin-width histogram, equal bin-height histogram, and similarity-based affinity propagation. Experimental results illustrate that: (i) Compared to the existing HDCluster, our proposed HDC-based clustering algorithms can achieve better accuracy, more robust performance, fewer iterations, and less execution time. Similarity-based affinity propagation outperforms the other three HDC-based clustering algorithms on eight datasets by 2% ~ 38% in clustering accuracy. (ii) Even for one-pass clustering, i.e., without any iterative update of the cluster hypervectors, our proposed algorithms can provide more robust clustering accuracy than HDCluster. (iii) Over eight datasets, five out of eight can achieve higher or comparable accuracy when projected onto the hyperdimensional space. Traditional clustering is more desirable than HDC when the number of clusters, $k$ , is large.

Scientific mapping has now become an important subject in the scientometrics field. Journal clustering can provide insights into both the internal relations among journals and the evolution trend of studies. In this paper, we apply the affinity propagation (AP) algorithm to do scientific journal clustering. The AP algorithm identifies clusters by detecting their representative points through message passing within the data points. Compared with other clustering algorithms, it can provide representatives for each cluster and does not need to pre-specify the number of clusters. Because the input of the AP algorithm is the similarity matrix among data points, it can be applied to various forms of data sets with different similarity metrics. In this paper, we extract the similarity matrices from the journal data sets in both cross citation view and text view and use the AP algorithm to cluster the journals. Through empirical analysis, we conclude that these two clustering results by the two single views are highly complementary. Therefore, we further combine text information with cross citation information by using the simple average scheme and apply the AP algorithm to conduct multi-view clustering. The multi-view clustering strategy aims at obtaining refined clusters by integrating information from multiple views. With text view and citation view integrated, experiments on the Web of Science journal data set verify that the AP algorithm obtains better clustering results as expected. [ABSTRACT FROM AUTHOR]

Conventional independent component analysis (ICA) monitoring methods extract the feature information of process data by selecting more important independent components (ICs), which discard a small part of ICs that may contain useful information for faults, leading to unsatisfactory monitoring results. However, when the number of sampling points is greater than that of process variables, the ICA monitoring model does not work well. To address the aforementioned problems, a novel monitoring method, multiphase enhanced high‐order information extraction (MEHOIE), is proposed in this paper. The entire production process was first divided into several steady phases and transition phases by the affinity propagation (AP) phase partitioning method. The enhanced high‐order information extraction (EHOIE) model was then built in each phase for fault monitoring. Finally, the algorithm was applied in the penicillin simulation platform and industrial microbial pharmaceutical process. The flexibility and superiority of this algorithm were verified by comparing it with other conventional methods. [ABSTRACT FROM AUTHOR]

Spectral clustering algorithm has become more popular in data clustering problems in recent years, due to the idea of optimally dividing the graph to solve the data clustering problems. However, the performance of the spectral clustering algorithm is affected by the quality of the similarity matrix. In addition, the traditional spectral clustering algorithm is unstable because it uses the K-means algorithm in the final clustering stage. Therefore, we propose a spectral clustering algorithm based on fast diffusion search for natural neighbor and affinity propagation (FDAP-SC). The algorithm obtains neighbor information more efficiently by changing the way of determining the number of neighbors. And it uses the shared nearest neighbors and the shared reverse neighbors between two points to construct the similarity matrix. Moreover, the algorithm regards all data points as nodes in the network and then calculates the clustering center of each sample through message passing between nodes. In this paper, we first experimentally on real datasets to verify that our proposed method for determining the number of neighbors outperforms the traditional natural nearest neighbor algorithm. We then demonstrate on synthetic datasets that FDAP-SC can handle complex shape datasets well. Finally, we compare FDAP-SC with several existing classical and novel algorithms on real datasets and Olivetti face datasets, proving the superiority and stability of FDAP-SC algorithm performance. Among the seven real datasets, FDAP-SC has the best performance on five datasets, and in the Olivetti face datasets, FDAP-SC achieves more than 87.5% accuracy. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor networks (WSNs), eg, industrial WSNs, require reliability and real‐time communication. Clustering technique together with schedule‐based access can provide the benefits, such as energy saving, reliability, and timeliness. However, integrating above two technologies into WSNs requires sophistical time slot allocation mechanism. To simplify the time slot allocation, the paper proposes a distributed interference‐free clustering algorithm for WSNs. The algorithm is inspired by affinity propagation (AP) clustering algorithm. By adapting and improving the original AP algorithm, the proposed clustering algorithm aims to jointly optimize energy saving and coverage issues while providing interference free between clusters. The performance analysis demonstrates that it can achieve high receiving rate (reliability), low delay (real time), and low‐energy consumption. [ABSTRACT FROM AUTHOR]

Multi-objective problems have gained much attention during the last decade. To balance the diversity and the convergence of the multi-objective differential evolution algorithm (MODE), an improved MODE is proposed based on the affinity propagation clustering (APC) and the non-dominated count approach in this paper. The proposed algorithm is referred to as AP-MODE, which improves the search efficiency by utilizing the affinity propagation approach to find out the population distribution structure for guiding search. In addition, mating restriction probability is used to select parent individuals for recombination from the neighborhoods or the whole population. Meanwhile, the mating restriction probability is updated according to the non-dominated count approach at each generation. This proposed algorithm is verified by comparing it with some state-of-the-art multi-objective evolutionary algorithms, and the simulation results on DTLZ test problems indicate that AP-MODE can efficiently achieve two goals of multi-objective optimization, i.e., the convergence to actual Pareto front and uniform spread of individuals along Pareto front. [ABSTRACT FROM AUTHOR]

Non-intrusive load monitoring (NILM) can monitor the operating state and energy consumption of appliances without deploying sub-meters and is promising to be widely used in residential communities. With the rapid increase of electric loads in amount and type, constructing representative load signatures and designing effective classification models are becoming increasingly crucial for NILM. In this paper, temporal and spectral load signatures that preserve sufficient information are constructed from the monitored energy data. The fusion of these two types of load signatures can provide rich distinguishing features for improving the performance of appliance recognition in NILM. Benefiting from the development of deep learning, this study proposes the two-stream convolutional neural networks (TSCNN) to extract the features from the two types of load signatures and perform classification. Furthermore, this study introduces the affinity propagation clustering strategy to mitigate the negative impact of intra-class variety mainly caused by multi-state loads in appliance recognition. The experimental results on public NILM datasets demonstrate that the proposed method outperforms most of the existing methods based on the voltage-current trajectory or recurrence graph in the recognition accuracy of submetered and aggregated measurements. [ABSTRACT FROM AUTHOR]

With the evolution of next generation sequencing (NGS) technologies, wholegenome sequencing of bacterial isolates is increasingly employed to investigate epidemiology. Phylogenetic analysis is the common method for using NGS data, usually for comparing closeness between bacterial isolates to detect probable outbreaks. However, interpreting a phylogenetic tree is not easy without training in evolutionary biology. Therefore, developing an easy-to-use tool that can assist people who wish to use a phylogenetic tree to investigate epidemiological relatedness is crucial. In this paper, we present a tool called OutbreakFinder that can accept a distance matrix in csv format; alignment files from Lyve-SET, Parsnp, and ClustalOmega; and a tree file in Newick format as inputs to compute a cluster-labeled two-dimensional plot based on multidimensional-scaling dimension reduction coupled with affinity propagation clustering. OutbreakFinder can be downloaded for free at https://github.com/skypes/Newton-method-MDS. [ABSTRACT FROM AUTHOR]

Unlike many traditional feature extraction methods of vibration signal such as ensemble empirical mode decomposition (EEMD), deep belief network (DBN) in deep learning can extract the useful information automatically and reduce the reliance on experts, with signal processing technology, and troubleshooting experience. In conventional fault diagnosis, data labels are required for classifiers such as support vector machine, random forest, and artificial neural networks. These are usually based on expert knowledge, for training and testing. But the process is usually tedious. The clustering model, on the other hand, can finish the roller bearings fault diagnosis without data labels, which is more efficient. There are some common clustering models which include fuzzy C-means (FCM), Gustafson–Kessel (GK), Gath–Geva (GG) models, and affinity propagation (AP). Unlike FCM, GK, and GG, which require knowledge or experience to pre-set the number of cluster center points, AP clustering algorithm can obtain the cluster center point according to the responsibility and availability calculations for all data points automatically. To the best of the authors' knowledge, AP is rarely used for fault diagnosis. In this paper, a method which combines DBN, with several hidden layers, and AP for roller bearings fault diagnosis is proposed. For data visualization, the principal component analysis (PCA) is deployed to reduce the dimension of the extracted feature. The first two principal components are employed as the input of the FCM, GK, GG, and AP models for roller bearings faults diagnosis. Compared with other combination models such as EEMD–PCA–FCM/GK/GG and DBN–PCA–FCM/GK/GG, the proposed method, from the experimental results, is superior to the aforementioned combination models. [ABSTRACT FROM AUTHOR]

Battery grouping is a technology widely used to improve the performance of battery packs. In this paper, we propose a time series clustering based battery grouping method. The proposed method utilizes the whole battery charge/discharge sequence for battery grouping. The time sequences are first denoised with a wavelet denoising technique. The similarity matrix is then computed with the dynamic time warping distance, and finally the time series are clustered with the affinity propagation algorithm according to the calculated similarity matrices. The silhouette index is utilized for assessing the performance of the proposed battery grouping method. Test results show that the proposed battery grouping method is effective. [ABSTRACT FROM AUTHOR]

Coordinated multipoint (CoMP) is corroborated to be an effective technology in mitigating cochannel interference (CCI) and enhancing system performance in picocell systems, which consist of a large number of pico base stations (BSs). In picocell systems, effective CoMP clustering schemes could provide significant gains of system performance such as throughput and cell-edge spectrum efficiency (SE). Moreover, an intrinsic problem of densely deployed networks is the cost of signaling overhead and data exchange between BSs in clusters. In this paper, a novel semidynamic clustering scheme based on affinity propagation for CoMP-Pico is presented to maximize user SE and throughput under the constraint of backhaul cost. Our proposed scheme consists of online and offline stages that can achieve good performance and low complexity. Simulation results show that the proposed scheme yields significant gains of SE and throughput and low running time compared with the existing clustering schemes. [ABSTRACT FROM PUBLISHER]

With the ever-increasing penetration level of renewable generation sources, a modern power system is facing more inevitable uncertainties that could lead to weakly damped oscillations. Detecting coherency among synchronous generators is one of the key steps of situational awareness for a given power system with a very high level of renewable penetration. In this paper, a wide-area measurement system (WAMS) based coherency detection algorithm employing the kernel principal component analysis (KPCA) and clustering analysis based on affinity propagation (AP) is proposed for a power system with extensive penetration of renewable generation sources. First, several trajectory similarity indexes are presented for determining the similarity between the trajectories of any two generators in the center of inertia coordinate. Second, a KPCA-based method is presented to integrate the trajectory similarity indexes for addressing the correlations among multiple indexes. Next, the AP-based clustering analysis method is utilized to detect the coherency among synchronous generators without the need of prespecifying the number of clusters. Finally, Southern China power system and a part of northern China power system with Zhangbei wind farms included, both with very high levels of renewable generation penetration, are utilized to demonstrate the proposed WAMS-based coherency detection methodology, and the application to actual Guangdong power system in south China to verify the applicability and practicality. [ABSTRACT FROM AUTHOR]

The perceptual hash algorithmis a technique to authenticate the integrity of images. While a few scholars have worked on mono-spectral image perceptual hashing, there is limited research on multispectral image perceptual hashing. In this paper, we propose a perceptual hash algorithm for the content authentication of a multispectral remote sensing image based on the synthetic characteristics of each band: firstly, the multispectral remote sensing image is preprocessed with band clustering and grid partition; secondly, the edge feature of the band subsets is extracted by band fusion-based edge feature extraction; thirdly, the perceptual feature of the same region of the band subsets is compressed and normalized to generate the perceptual hash value. The authentication procedure is achieved via the normalized Hamming distance between the perceptual hash value of the recomputed perceptual hash value and the original hash value. The experiments indicated that our proposed algorithm is robust compared to content-preserved operations and it efficiently authenticates the integrity of multispectral remote sensing images. [ABSTRACT FROM AUTHOR]

In this paper, we propose an enhanced affinity propagation (AP)-based resource allocation scheme (APRA) to overcome major issues in machine-to-machine (M2M), such as delay, complexity, throughput, and system capacity. There would be rapid increase of added devices, such as cellular and machine-type devices. It would be difficult for Evolved Node B (eNB) to control all of them. Considering this problem, we propose an AP-based group formation method in which machines make groups with other similar type of machines. After making groups, group members in each group can communicate directly with each other by getting a channel from eNB via their group head. A resource allocation method is proposed for different groups that can use the same channel at the same time. Considering energy constraints, we also propose different methods to rotate the role of a group head among group members, through the modification of AP or the application of Markov chain model. As expected, the group head will drain energy at a higher rate than the group members. Thus, the rotation of the group head will increase the overall performance. Simulation results show that the proposed method can minimize both data delivery delay and operation complexity while increasing the throughput, system capacity, and energy efficiency through the rotation of the group head. [ABSTRACT FROM AUTHOR]

Estimating the optimal number of clusters (NC) is pivotal in cluster analysis. From the viewpoint of sample geometry, a novel internal clustering validity index, which is termed the between-within cluster (BWC) index, is designed in this paper. Moreover, a method is proposed to estimate the optimal NC. The BWC index improves the well-known Silhouette index. BWC validates the clustering results from a certain clustering algorithm (e.g., affinity propagation or hierarchical) and estimates the optimal NC for many kinds of data sets, including synthetic data sets, benchmark data sets, UCI data sets, gene expression data sets, and images. Theoretical analysis and experimental studies demonstrate the effectiveness and high efficiency of the new index and method. [ABSTRACT FROM AUTHOR]

Dense small cells have been considered as a promising solution for improving system throughput in 5G networks. When a large number of small cells are overlapped, serious inter-cell interference (ICI) becomes one of the major technical challenges. Coordinated multi-point (CoMP) is an effective technology to mitigate ICI. In this paper, we study affinity propagation based CoMP cluster for dense small cell networks with backhaul constrains. The graph based static clusters and affinity propagation based clusters are formed so as to achieve good performance and reduce complexity. By taking into account the hybrid CoMP mode, the optimal problem which maximizes the system throughput with the limit-capacity backhaul has been formulated. An effective hybrid CoMP cluster strategy is gained by solving the optimal problem. The simulation results demonstrate that the proposed scheme could enhance average throughput. [ABSTRACT FROM AUTHOR]

Clustering is a crucial step in scientific data analysis and engineering systems. Thus, an efficient cluster analysis method often remains a key challenge. In this paper, we introduce a general purpose exemplar-based clustering method called (MEGA), which performs a novel message-passing strategy based on variational expectation–maximization and generalized arc-consistency techniques. Unlike message passing clustering methods, MEGA formulates the message-passing schema as E- and M-steps of variational expectation–maximization based on a reparameterized factor graph. It also exploits an adaptive variant of generalized arc consistency technique to perform a variational mean-field approximation in E-step to minimize a Kullback–Leibler divergence on the model evidence. Dissimilar to density-based clustering methods, MEGA has no sensitivity to initial parameters. In contrast to partition-based clustering methods, MEGA does not require pre-specifying the number of clusters. We focus on the binary-variable factor graph to model the clustering problem but MEGA is applicable to other graphical models in general. Our experiments on real-world problems demonstrate the efficiency of MEGA over existing prominent clustering algorithms such as Affinity propagation, Agglomerative, DBSCAN, K-means, and EM. [ABSTRACT FROM AUTHOR]

The bag of visual words (BOW) model has attracted a great deal of attention in high-resolution remote sensing image classification. Related studies based on polarimetric synthetic aperture radar (PolSAR) image classification are also still challenging. This paper presents a new method of PolSAR image classification based on the BOW model and supplemented by an affinity propagation clustering algorithm (AP), as well as a spatial pyramid matching (SPM) technique (AP_SPM_BOW). Its classification effect is analyzed compared to those of BOW models with other clustering algorithms (e.g., K-means, meanshift, Gaussian mixture model), as well as the ML classification method by using two types of PolSAR images with different resolutions collected from EMISAR and RADARSAT-2 sensors. These results demonstrate that the proposed AP_SPM_BOW model is not only a stable and effective tool for PolSAR image classification but also more suitable for the analysis of high-resolution PolSAR images. In addition, the use of polarimetric information in BOW models can effectively improve the classification accuracy. [ABSTRACT FROM PUBLISHER]

Affinity propagation (AP) is a recently proposed clustering algorithm, which has been successful used in a lot of practical problems. Although effective in finding meaningful clustering solutions, a key disadvantage of AP is its efficiency, which has become the bottleneck when applying AP for large-scale problems. In the literature, most of the methods proposed to improve the efficiency of AP are based on implementing the message-passing on a sparse similarity matrix, while neither the decline in effectiveness nor the improvement in efficiency is theoretically analyzed. In this paper, we propose a two-stage fast affinity propagation (FastAP) algorithm. Different from previous work, the scale of the similarity matrix is first compressed by selecting only potential exemplars, then further reduced by sparseness according to k nearest neighbors. More importantly, we provide theoretical analysis, based on which the improvement of efficiency in our method is controllable with guaranteed clustering performance. In experiments, two synthetic data sets, seven publicly available data sets, and two real-world streaming data sets are used to evaluate the proposed method. The results demonstrate that FastAP can achieve comparable clustering performances with the original AP algorithm, while the computational efficiency has been improved with a several-fold speed-up on small data sets and a dozens-of-fold on larger-scale data sets. [ABSTRACT FROM AUTHOR]

Class imbalance problem poses a difficulty to learning algorithms in pattern classification. Oversampling techniques is one of the most widely used techniques to solve these problems, but the majority of them use the sample size ratio as an imbalanced standard. This paper proposes a fuzzy representativeness difference-based oversampling technique, using affinity propagation and the chromosome theory of inheritance (FRDOAC). The fuzzy representativeness difference (FRD) is adopted as a new imbalance metric, which focuses on the importance of samples rather than the number. FRDOAC firstly finds the representative samples of each class according to affinity propagation. Secondly, fuzzy representativeness of every sample is calculated by the Mahalanobis distance. Finally, synthetic positive samples are generated by the chromosome theory of inheritance until the fuzzy representativeness difference of two classes is small. A thorough experimental study on 16 benchmark datasets was performed and the results show that our method is better than other advanced imbalanced classification algorithms in terms of various evaluation metrics. [ABSTRACT FROM AUTHOR]

Dealing with dynamic data stream has become one of the most active research fields for Internet of Things (IoT). Specifically, clustering toward dynamic data stream is a necessary foundation for numerous IoT platforms. In this paper, we focus on dynamic exemplar-based clustering models. In terms of the maximum a priori principle, under the probability framework, we first summarize a unified explanation for two typical exemplar-based clustering models, namely enhanced α -expansion move (EEM) and affinity propagation (AP). Then, a new dynamic exemplar-based data stream clustering algorithm called DSC is proposed accordingly. The distinctive merit of the proposed algorithm DSC is that we can simply utilize the framework of EEM algorithm through modifying the definitions of several variables and do not need to design another optimization mechanism. Moreover, algorithm DSC is capable of dealing to two cases of similarities. In contrast to both AP and EEM, our experimental results indicate the power of algorithm DSC for real-world IoT data streams. [ABSTRACT FROM AUTHOR]

The United States is a diverse and heterogeneous place. Accurately organizing and mapping the U.S. into different regions based on characteristics such as wealth, race, education, language, and occupation is a complicated and arduous task. This paper demonstrates the application of affinity propagation to map socio-economic patterns and identify representative exemplars. Affinity propagation clusters data based on representative exemplars and considers all data points as potential cluster exemplars. We use socio-economic data from the United States census to cluster zip codes tabulation areas and identify representative locations of socio-economic diversity of the United States. The 11 socio-economic clusters were mapped individually and together using area-based generalization. Mapping the results illustrated distinct regionalization and historical migration trends within the United States as well as national urban/suburban/rural patterns. Future applications of this technique may be useful for data-driven socio-economic analysis and purposive sampling. [ABSTRACT FROM AUTHOR]

Due to electromagnetic silence, passive tracking systems for emitter targets usually produce track segments (i.e., tracklets) rather than an entire trajectory of the target. Therefore, a multistage method for emitter target tracking is proposed in this paper. In the stage of tracklet generation, the Gaussian mixture-probability hypothesis density tracker with adaptive estimation of target birth intensity is applied to generate reliable tracklets of the emitter targets. After that, in the stage of tracklet association, the multipoint motion information and emitter signal information are integrated to compute the similarities between the tracklets. The affinity propagation algorithm, which does not impose the constraint of one-to-one correspondence, is then used to cluster the tracklets. In the stage of association refining, the clustering result is adjusted to refine the final trajectories according to the spatial-temporal constraint of the tracklets. The simulation results show that the proposed method is robust and performs well. [ABSTRACT FROM PUBLISHER]

Affinity propagation is a message‐passing‐based clustering procedure that has received widespread attention in domains such as biological science, physics, and computer science. However, its implementation in psychology and related areas of social science is comparatively scant. In this paper, we describe the basic principles of affinity propagation, its relationship to other clustering problems, and the types of data for which it can be used for cluster analysis. More importantly, we identify the strengths and weaknesses of affinity propagation as a clustering tool in general and highlight potential opportunities for its use in psychological research. Numerical examples are provided to illustrate the method. [ABSTRACT FROM AUTHOR]

This paper develops a distributed strategy to identify an energy-efficient base station (BS) network configuration for green cellular networks. During off-peak periods where traffic demands are only a fraction of the peak-time traffic demands, a subset of BSs is switched off to minimize operational energy consumption without affecting service to any of network users. To this end, we formulate a combinatorial optimization of jointly determining BS switching and user association. This formulation, however, requires a computationally demanding task as the population of the network grows. To resolve these challenges, we introduce a graphical-model approach to the optimization formulation and derive a distributed algorithm based on affinity propagation, which is a message-passing algorithm developed for data clustering in data-mining techniques. The proposed algorithm operates via simple local information exchanges among users and BSs and provides a very efficient solution for energy-saving management with low computational costs. We also present a green protocol that transforms commercial cellular networks into green radio networks using the proposed algorithm. Simulation results verify that the developed solution significantly improves the energy savings and resource utilization in the network. [ABSTRACT FROM PUBLISHER]

An approach for identification of terahertz (THz) spectral of genetically modified organisms (GMOs) based on active learning affinity propagation clustering algorithm (ALAP) combined with support vector machine (SVM) in this paper, and THz transmittance spectra of some typical genetically modified (GM) cotton samples are investigated to prove its feasibility. Firstly, principal component analysis is applied to extract features of the spectrum data. Secondly, instead of the original spectrum data, the feature signals are fed into the ALAP-SVM pattern recognition, where an improved active learning ALAP is applied to SVM. The experimental results show that THz spectroscopy combined with ALAP-SVM can be effectively utilized for identification of different GM cottons. The proposed approach provides a new effective method for detection and identification of different GMOs by using THz spectroscopy. [ABSTRACT FROM AUTHOR]

Affinity Propagation (AP) clustering has been successfully used in a lot of clustering problems. However, most of the applications deal with static data. This paper considers how to apply AP in incremental clustering problems. First, we point out the difficulties in Incremental Affinity Propagation (IAP) clustering, and then propose two strategies to solve them. Correspondingly, two IAP clustering algorithms are proposed. They are IAP clustering based on K-Medoids (IAPKM) and IAP clustering based on Nearest Neighbor Assignment (IAPNA). Five popular labeled data sets, real world time series and a video are used to test the performance of IAPKM and IAPNA. Traditional AP clustering is also implemented to provide benchmark performance. Experimental results show that IAPKM and IAPNA can achieve comparable clustering performance with traditional AP clustering on all the data sets. Meanwhile, the time cost is dramatically reduced in IAPKM and IAPNA. Both the effectiveness and the efficiency make IAPKM and IAPNA able to be well used in incremental clustering tasks. [ABSTRACT FROM AUTHOR]