• A graph learning framework, which captures both the global and local structure in data, is proposed. Theoretical analysis builds connections of our model to k-means, spectral clustering, kernel k-means. Extensions semi-supervised classification multiple are presented. Graphs have become increasingly popular modeling structures interactions a wide variety problems during last decade. Graph-bas...

Huge amount of data are nowadays produced by a large and disparate family sensors, which typically measure multiple variables over time. Such rich information can be profitably organized as multivariate time-series. Collect enough labelled samples to set up supervised analysis for such kind is challenging while reasonable assumption dispose limited background knowledge that injected in the proc...

The problem of unsupervised and semi-supervised clustering is extensively studied in machine learning. In order to involve user in image data clustering, (Lai et al., 2014) proposed a new approache for interactive semi-supervised clustering that translates the feedback of user (expressed at the level of individual images) into pairwise constraints between groups of images, these groupes being c...

Researches concerning (semi-)supervised clustering are recently emerging. We show two types of clustering tasks which should be axiomatically differentiated under this

Clustering is the process which is used to assign a set of n objects into clusters(groups). Dimensionality reduction techniques help in increasing the accuracy of clustering results by removing redundant and irrelevant dimensions. But, in most of the situations, objects can be related in different ways in different subsets of the dimensions. Dimensionality reduction tends to get rid of such rel...

This paper elaborates the approach used by the Applied Data Mining Research Group (ADMRG) for the Social Event Detection (SED) Tasks of the 2013 MediaEval Benchmark. We participated in the semi-supervised clustering task as well as the classification of social events task. The constrained clustering algorithm is utilized in the semi-supervised clustering task. Several machine learning classifie...