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ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Relationship-based clustering and cluster ensembles for high-dimensional data mining
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IEEE Transactions on Computers
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Collaborative clustering with the use of Fuzzy C-Means and its quantification
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Metastructural facets of granular computing
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Intelligent Data Analysis
When Semi-supervised Learning Meets Ensemble Learning
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
A novel hierarchical-clustering-combination scheme based on fuzzy-similarity relations
IEEE Transactions on Fuzzy Systems
Nonparametric Bayesian clustering ensembles
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
PSO driven collaborative clustering: A clustering algorithm for ubiquitous environments
Intelligent Data Analysis - Ubiquitous Knowledge Discovery
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Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Cluster-Based cumulative ensembles
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
From cluster ensemble to structure ensemble
Information Sciences: an International Journal
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HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
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International Journal of Business Information Systems
Projective clustering ensembles
Data Mining and Knowledge Discovery
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ACM SIGKDD Explorations Newsletter
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In this paper, we present a multiple data clusterings combiner, based on a proposed Weighted Shared nearest neighbors Graph (WSnnG). While combining of multiple classifiers (supervised learners) is now an active and mature area, only a limited number of contemporary research in combining multiple data clusterings (unsupervised learners) appear in the literature. The problem addressed in this paper is that of generating a reliable clustering to represent the natural cluster structure in a set of patterns, when a number of different clusterings of the data is available or can be generated. The underlying model of the proposed shared nearest neighbors based combiner is a weighted graph, whose vertices correspond to the set of patterns, and are assigned relative weights based on a ratio of a balancing factor to the size of their shared nearest neighbors population. The edges in the graph exist only between patterns that share a pre-specified portion of their nearest neighborhood. The graph can be further partitioned into a desired number of clusters. Preliminary experiments show promising results, and comparison with a recent study justifies the combiner's suitability to the pre-defined problem domain.