Neural Computation
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 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
Clustering and singular value decomposition for approximate indexing in high dimensional spaces
Proceedings of the seventh international conference on Information and knowledge management
Fast algorithms for projected clustering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Mixtures of probabilistic principal component analyzers
Neural Computation
Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Clustering by pattern similarity in large data sets
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
A Monte Carlo algorithm for fast projective clustering
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Feature Subset Selection and Order Identification for Unsupervised Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Locally adaptive techniques for pattern classification
Locally adaptive techniques for pattern classification
Horizontal aggregations for building tabular data sets
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Programming the K-means clustering algorithm in SQL
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Integrating K-Means Clustering with a Relational DBMS Using SQL
IEEE Transactions on Knowledge and Data Engineering
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The clustering problem concerns the discovery of homogeneous groups of data according to a certain similarity measure. Clustering suffers from the curse of dimensionality. It is not meaningful to look for clusters in high dimensional spaces as the average density of points anywhere in input space is likely to be low. As a consequence, distance functions that equally use all input features may be ineffective. We introduce an algorithm that discovers clusters in subspaces spanned by different combinations of dimensions via local weightings of features. This approach avoids the risk of loss of information encountered in global dimensionality reduction techniques. Our method associates to each cluster a weight vector, whose values capture the relevance of features within the corresponding cluster. In this paper we present an efficient SQL implementation of our algorithm, that enables the discovery of clusters on data residing inside a relational DBMS.