A cost model for nearest neighbor search in high-dimensional data space
PODS '97 Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
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
Data mining: concepts and techniques
Data mining: concepts and techniques
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Subspace clustering for high dimensional data: a review
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Learning correlations using the mixture-of-subsets model
ACM Transactions on Knowledge Discovery from Data (TKDD)
SS-ClusterTree: a subspace clustering based indexing algorithm over high-dimensional image features
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Automatic parameter determination in subspace clustering with gravitation function
Proceedings of the Fourteenth International Database Engineering & Applications Symposium
Making interval-based clustering rank-aware
Proceedings of the 14th International Conference on Extending Database Technology
A new clustering algorithm with the convergence proof
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part I
Feature interaction in subspace clustering using the Choquet integral
Pattern Recognition
A clustering ensemble framework based on elite selection of weighted clusters
Advances in Data Analysis and Classification
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Recently data mining applications require a large amount of high-dimensional data. However, most clustering methods for data miming do not work efficiently for dealing with large, high-dimensional data because of the so-called 'curse of dimensionality'[1] and the limitation of available memory. In this paper, we propose a new cell-based clustering method which is more efficient for large, high-dimensional data than the existing clustering methods. Our clustering method provides an efficient cell creation algorithm using a space-partitioning technique and uses a filtering-based index structure using an approximation technique. Finally, we compare the performance of our cell-based clustering method with the CLIQUE method in terms of cluster construction time, precision, and retrieval time. The experimental results show that our clustering method achieves better performance on cluster construction time and retrieval time.