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
Finding generalized projected clusters in high dimensional spaces
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Computing Clusters of Correlation Connected objects
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Streaming pattern discovery in multiple time-series
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Deriving quantitative models for correlation clusters
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
CARE: Finding Local Linear Correlations in High Dimensional Data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Finding multiple global linear correlations in sparse and noisy data sets
Knowledge-Based Systems
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Finding linear correlations in dataset is an important data mining task, which can be widely applied in the real world. Existing correlation clustering methods may miss some correlations when instances are sparsely distributed. Other recent studies are limited to find the primary linear correlation of the dataset. This paper develops a novel approach to seek multiple local linear correlations in dataset. Extensive experiments show that this approach is effective and efficient to find the linear correlations in data subsets.