Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
CACTUS—clustering categorical data using summaries
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
ROCK: a robust clustering algorithm for categorical attributes
Information Systems
Concept decompositions for large sparse text data using clustering
Machine Learning
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Semi-supervised Clustering by Seeding
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
INCONCO: interpretable clustering of numerical and categorical objects
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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We propose a new methodology for clustering data comprising multiple domains or parts, in such a way that the separate domains mutually supervise each other within a semi-supervised learning framework. Unlike existing uses of semi-supervised learning, our methodology does not assume the presence of labels from part of the data, but rather, each of the different domains of the data separately undergoes an unsupervised learning process, while sending and receiving supervised information in the form of data constraints to/from the other domains. The entire process is an alternation of semi-supervised learning stages on the different data domains, based on Basu et al.'s Hidden Markov Random Fields (HMRF) variation of the K-means algorithm for semi-supervised clustering that combines the constraint-based and distance-based approaches in a unified model. Our experiments demonstrate a successful mutual semi-supervision between the different domains during clustering, that is superior to the traditional heterogeneous domain clustering baselines consisting of converting the domains to a single domain or clustering each of the domains separately.