Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
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
Data mining: concepts and techniques
Data mining: concepts and techniques
Graphical Models: Foundations of Neural Computation
Graphical Models: Foundations of Neural Computation
A Scalable Approach to Balanced, High-Dimensional Clustering of Market-Baskets
HiPC '00 Proceedings of the 7th International Conference on High Performance Computing
When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
An Algorithm for Non-Distance Based Clustering in High Dimensional Spaces
An Algorithm for Non-Distance Based Clustering in High Dimensional Spaces
A survey on wavelet applications in data mining
ACM SIGKDD Explorations Newsletter
Document clustering via adaptive subspace iteration
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
A general model for clustering binary data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Association-based similarity testing and its applications
Intelligent Data Analysis
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The clustering problem, which aims at identifying the distribution of patterns and intrinsic correlations in large data sets by partitioning the data points into similarity clusters, has been widely studied. Traditional clustering algorithms use distance functions to measure similarity and are not suitable for high dimensional spaces. In this paper, we propose CoFD algorithm, which is a non-distance based clustering algorithm for high dimensional spaces. Based on the maximum likelihood principle, CoFD is to optimize parameters to maximize the likelihood between data points and the model generated by the parameters. Experimental results on both synthetic data sets and a real data set show the efficiency and effectiveness of CoFD.