In this paper we present our system and experiments at the Third Web People Search Workshop (WePS-3) task for clustering web people search documents in English. In our experiments we used a simple approach with three algorithms: Lingo, Hierachical Agglomerative Clustering (HAC), and a 2-step HAC algorithm. We also present the results and initial conclusions in the context of the WePS-3 Task 1 f...

In this paper, a method for anonymization of social networks by clustering of k-edge-connected subgraphs (SACK) is presented. Previous anonymization algorithms do not consider distribution of nodes in social network graph according to their attributes. SACk tries to focus on this aspect that probability of existence of an edge between two nodes is related to their attributes and this leads to a...

Hierarchical clustering constructs a hierarchy of clusters by either repeatedly merging two smaller clusters into a larger one or splitting a larger cluster into smaller ones. The crucial step is how to best select the next cluster(s) to split or merge. Here we provide a comprehensive analysis of selection methods and propose several new methods. We perform extensive clustering experiments to t...

We propose a novel approach for data clustering based on sequential multi-objective multi-act games (ClusSMOG). It automatically determines the number of clusters and optimises simultaneously the inertia and the connectivity objectives. The approach consists of three structured steps. The first step identifies initial clusters and calculates a set of conflict-clusters. In the second step, for e...

In this paper, we present a method of face recognition based on fuzzy clustering and parallel neural networks. Two sets of images are acquired: the frontal images and the slight angle profile face images. In the case of the slight angle profile face images,a head model is built in order to compensate the pose. In the second step, we propose a method for face recognition based on fuzzy clusterin...

In this paper we propose a clustering procedure aimed at grouping time series with an association between extremely low values, measured by the lower tail dependence coefficient. Firstly, we estimate the coefficient using an Archimedean copula function. Then, we propose a dissimilarity measure based on tail dependence coefficients and a two-step procedure to be used with clustering algorithms w...

Conventional clustering algorithms utilize a single criterion that may not conform to the diverse shapes of the underlying clusters. We offer a new clustering approach that uses multiple clustering objective functions simultaneously. The proposed multiobjective clustering is a two-step process. It includes detection of clusters by a set of candidate objective functions as well as their integrat...