Deriving quantitative models for correlation clusters
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A probabilistic majorclust variant for the clustering of near-homogeneous graphs
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
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We propose a new approach to clustering high dimensional data based on a novel notion of cluster cores, instead of on nearest neighbors. A cluster core is a fairly dense group with a maximal number of pairwise similar objects. It represents the core of a cluster, as all objects in a cluster are with a great degree attracted to it. As a result, building clusters from cluster cores achieves high accuracy. Other major characteristics of the approach include: (1) It uses a semantics-based similarity measure. (2) It does not incur the curse of dimensionality and is scalable linearly with the dimensionality of data. (3) It outperforms the well-known clustering algorithm, ROCK, with both lower time complexity and higher accuracy.