BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Robust information-theoretic clustering
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
An Entropy Weighting k-Means Algorithm for Subspace Clustering of High-Dimensional Sparse Data
IEEE Transactions on Knowledge and Data Engineering
A k-mean clustering algorithm for mixed numeric and categorical data
Data & Knowledge Engineering
Outlier-robust clustering using independent components
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
A Weight Entropy k-Means Algorithm for Clustering Dataset with Mixed Numeric and Categorical Data
FSKD '08 Proceedings of the 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 01
An information-theoretic external cluster-validity measure
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Clustering based on compressed data for categorical and mixed attributes
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Clustering mixed type attributes in large dataset
ISPA'05 Proceedings of the Third international conference on Parallel and Distributed Processing and Applications
INCONCO: interpretable clustering of numerical and categorical objects
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Cluster validity measures based on the minimum description length principle
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part I
Dependency clustering across measurement scales
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Integrative mining of heterogeneous data is one of the major challenges for data mining in the next decade. We address the problem of integrative clustering of data with mixed type attributes. Most existing solutions suffer from one or both of the following drawbacks: Either they require input parameters which are difficult to estimate, or/and they do not adequately support mixed type attributes. Our technique INTEGRATE is a novel clustering approach that truly integrates the information provided by heterogeneous numerical and categorical attributes. Originating from information theory, the Minimum Description Length (MDL) principle allows a unified view on numerical and categorical information and thus naturally balances the influence of both sources of information in clustering. Moreover, supported by the MDL principle, parameter-free clustering can be performed which enhances the usability of INTEGRATE on real world data. Extensive experiments demonstrate the effectiveness of INTEGRATE in exploiting numerical and categorical information for clustering. As an efficient iterative algorithm INTEGRATE is scalable to large data sets.