Learning multiple nonredundant clusterings
ACM Transactions on Knowledge Discovery from Data (TKDD)
Pairwise probabilistic clustering using evidence accumulation
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
A metric to evaluate a cluster by eliminating effect of complement cluster
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
A new asymmetric criterion for cluster validation
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
A max metric to evaluate a cluster
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
A clustering ensemble based on a modified normalized mutual information metric
AMT'12 Proceedings of the 8th international conference on Active Media Technology
Pairwise similarity for cluster ensemble problem: link-based and approximate approaches
Transactions on Large-Scale Data- and Knowledge-centered systems IX
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Each clustering algorithm induces a similarity between given data points, according to the underlying clustering criteria. Given the large number of available clustering techniques, one is faced with the following questions: (a) Which measure of similarity should be used in a given clustering problem? (b) Should the same similarity measure be used throughout the d-dimensional feature space? In other words, are the underlying clusters in given data of similar shape? Our goal is to learn the pairwise similarity between points in order to facilitate a proper partitioning of the data without the a priori knowledge of k, the number of clusters, and of the shape of these clusters. We explore a clustering ensemble approach combined with cluster stability criteria to selectively learn the similarity from a collection of different clustering algorithms with various parameter configurations.