Identification of distinct clusters of documents in text collections has traditionally been addressed by making the assumption that the data instances can only be represented by ...
Levent Bolelli, Seyda Ertekin, Ding Zhou, C. Lee G...
Abstract. This paper presents a probabilistic model for combining cluster ensembles utilizing information theoretic measures. Starting from a co-association matrix which summarizes...
Given the recent explosion of interest in streaming data and online algorithms, clustering of time series subsequences, extracted via a sliding window, has received much attention...
Spectral clustering algorithms have been shown to be more effective in finding clusters than some traditional algorithms such as k-means. However, spectral clustering suffers fro...
Spectral clustering is a widely used method for organizing data that only relies on pairwise similarity measurements. This makes its application to non-vectorial data straightforw...
Fabian L. Wauthier, Nebojsa Jojic, Michael I. Jord...