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The ubiquity of personal computing technology has produced an abundance of staggeringly large data sets — the Library of Congress has stored over 160 terabytes of web data and it is estimated that Facebook alone logs over 25 terabytes of data per day. There is a great need for systems by which one can elucidate the similarity and dissimilarity among and between groups in these data sets. Clustering...
This work focuses on the scalability of the Evidence Accumulation Clustering (EAC) method. We first address the space complexity of the co-association matrix. The sparseness of the matrix is related to the construction of the clustering ensemble. Using a split and merge strategy combined with a sparse matrix representation, we empirically show that a linear space complexity is achievable in this framework,...
Clustering is primarily used to uncover the true underlying structure of a given data set and, for this purpose, it is desirable to subject the same data to several different clustering algorithms. This paper attempts to put an order on the various partitions of a data set obtained from different clustering algorithms. The goodness of each partition is expressed by means of a performance measure based...
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