Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Balancing histogram optimality and practicality for query result size estimation
SIGMOD '95 Proceedings of the 1995 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
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
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 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
ACM Computing Surveys (CSUR)
Finding generalized projected clusters in high dimensional spaces
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A human-computer cooperative system for effective high dimensional clustering
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A Monte Carlo algorithm for fast projective clustering
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Redefining Clustering for High-Dimensional Applications
IEEE Transactions on Knowledge and Data Engineering
Pincer Search: A New Algorithm for Discovering the Maximum Frequent Set
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Optimal Grid-Clustering: Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering
VLDB '99 Proceedings of the 25th 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
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Finding Interesting Associations without Support Pruning
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Efficient Algorithm for Projected Clustering
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Mining Quantitative Frequent Itemsets Using Adaptive Density-Based Subspace Clustering
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Projected outlier detection in high-dimensional mixed-attributes data set
Expert Systems with Applications: An International Journal
Discovering pattern-based subspace clusters by pattern tree
Knowledge-Based Systems
Subspace and projected clustering: experimental evaluation and analysis
Knowledge and Information Systems
A fast algorithm for finding correlation clusters in noise data
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Advancing data clustering via projective clustering ensembles
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Projective clustering ensembles
Data Mining and Knowledge Discovery
Color Image Segmentation: From the View of Projective Clustering
International Journal of Multimedia Data Engineering & Management
A hierarchical semantic-based distance for nominal histogram comparison
Data & Knowledge Engineering
Subspace clustering of high-dimensional data: an evolutionary approach
Applied Computational Intelligence and Soft Computing
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Recent research suggests that clustering for high-dimensional data should involve searching for "hidden驴 subspaces with lower dimensionalities, in which patterns can be observed when data objects are projected onto the subspaces. Discovering such interattribute correlations and location of the corresponding clusters is known as the projective clustering problem. In this paper, we propose an efficient projective clustering technique by histogram construction (EPCH). The histograms help to generate "signatures,驴 where a signature corresponds to some region in some subspace, and signatures with a large number of data objects are identified as the regions for subspace clusters. Hence, projected clusters and their corresponding subspaces can be uncovered. Compared to the best previous methods to our knowledge, this approach is more flexible in that less prior knowledge on the data set is required, and it is also much more efficient. Our experiments compare behaviors and performances of this approach and other projective clustering algorithms with different data characteristics. The results show that our technique is scalable to very large databases, and it is able to return accurate clustering results.