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In this paper, we propose a solution on microaggregation problem based on the hierarchical tree equi-partition (HTEP) algorithm. Microaggregation is a family of methods for statistical disclosure control of microdata, that is, for masking microdata, so that they can be released without disclose private information on the underlying individuals. Knowing that the microaggregation problem is non-deterministic polynomial-time-hard, the goal is to partitionNgiven data into groups of at leastKitems, so that the sum of the within-partition squared error is minimized. The proposed method is general and it can be applied to any tree partition problem aiming at the minimization of a total score. The method is divisive, so that the tree with the highest ‘score’ is split into two trees, resulting in a hierarchical forest of trees with almost equal ‘score’ (equipartition). We propose a version of HTEP for microaggregation (HTEPM), that is applied on the minimum spanning tree (MST) of the graph defined by the data. The merit of the HTEPM algorithm is that it solves optimally some instances of the multivariate microaggregation problem on MST search space in. Experimental results and comparisons with existing methods from literature prove the high performance and robustness of HTEPM. [ABSTRACT FROM AUTHOR]

One of the fundamental problems in social networking with a lot of potential applications is to detect effectively the communities that are created by the users' interaction. Other applications, such as finding web communities, uncovering the structure of social networks, or even analyzing a graph's structure to uncover Internet attacks are equally as important. All these problems converge to a common goal, the development of flexible and efficient local community detection algorithms. In this paper, we demonstrate the performance of an algorithm that uncovers the entire community structure of a network, based solely on local interactions between neighboring nodes and a distributed clustering algorithm. The proposed algorithm, named VCD, is based on the distributed computation of a synthetic coordinates for each graph node. Experimental results and comparisons with another method from the literature (Lancichinetti et al.) are presented. The algorithm is also tested on two real dataset graphs from the SNAP: Stanford Large Network Dataset Collection. In all cases the experimental results demonstrate the high performance of our algorithm in terms of accuracy to detect communities, and its computational efficiency. [ABSTRACT FROM AUTHOR]

We propose a general purpose image segmentation framework, which involves feature extraction and classification in feature space, followed by flooding and merging in spatial domain. Region growing is based on the computed local measurements and distances from the distribution of features describing the different classes. Using the properties of the label dependent distances spatial coherence is ensured, since the image features are described globally. The distribution of the features for the different classes are obtained by block-wise unsupervised clustering based on the construction of the minimum spanning tree of the blocks' grid using the Mallows distance and the equipartition of the resulting tree. The final clustering is obtained by using the k-centroids algorithm. With high probability and under topological constraints, connected components of the maximum likelihood classification map are used to compute a map of initially labelled pixels. An efficient flooding algorithm is introduced, namely, Priority Multi-Class Flooding Algorithm (PMCFA), that assign pixels to labels using Bayesian dissimilarity criteria. A new region merging method, which incorporates boundary information, is introduced for obtaining the final segmentation map. Therefore, the merging stage is based on region features and edge localization. Segmentation results on the Berkeley benchmark data set demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM PUBLISHER]

We present a key frames selection algorithm based on three Iso-content principles (Iso-Content Distance, Iso-Content Error and Iso-Content Distortion), so that the selected key frames are equidistant-in video content according to the used principle. Two automatic approaches for defining the most appropriate number of key frames are proposed by exploiting supervised and unsupervised content criteria. Experimental results and the comparisons with existing methods from literature on large dataset of real-life video sequences illustrate the high performance of the proposed schemata. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we present an algorithm based on equal errors principle, which solves the general version of polygonal approximation problem (GPA). The approximation nodes of GPA can be selected anywhere on the original polygonal curve, while the “classical” (usually used) polygonal approximation problem (PA) demands the vertices to be a subset of the original polygon vertices. Although the proposed algorithm does not guarantee global minimum distortion, in many cases almost optimal solutions are achieved. Moreover, it is very flexible on changes of error criteria and on curve dimension yielding an alternative and in many cases approximations with lower error (by relaxing the simplification constraint) than the optimal PA methods with about the same computation cost. [Copyright &y& Elsevier]