Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Fast algorithms for projected clustering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Entropy-based subspace clustering for mining numerical data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Finding generalized projected clusters in high dimensional spaces
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A general probabilistic framework for clustering individuals and objects
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering through decision tree construction
Proceedings of the ninth international conference on Information and knowledge management
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A new cell-based clustering method for large, high-dimensional data in data mining applications
Proceedings of the 2002 ACM symposium on Applied computing
A Monte Carlo algorithm for fast projective clustering
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
d-Clusters: Capturing Subspace Correlation in a Large Data Set
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Information Theoretic Clustering of Sparse Co-Occurrence Data
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A generalized maximum entropy approach to bregman co-clustering and matrix approximation
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
IEEE Transactions on Signal Processing
Mixture models for learning low-dimensional roles in high-dimensional data
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
A global local modeling of internet usage in large mobile societies
Proceedings of the 7th ACM workshop on Performance monitoring and measurement of heterogeneous wireless and wired networks
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Using a mixture of random variables to model data is a tried-and-tested method common in data mining, machine learning, and statistics. By using mixture modeling it is often possible to accurately model even complex, multimodal data via very simple components. However, the classical mixture model assumes that a data point is generated by a single component in the model. A lot of datasets can be modeled closer to the underlying reality if we drop this restriction. We propose a probabilistic framework, the mixture-of-subsets (MOS) model, by making two fundamental changes to the classical mixture model. First, we allow a data point to be generated by a set of components, rather than just a single component. Next, we limit the number of data attributes that each component can influence. We also propose an EM framework to learn the MOS model from a dataset, and experimentally evaluate it on real, high-dimensional datasets. Our results show that the MOS model learned from the data represents the underlying nature of the data accurately.