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
Efficient computation of Iceberg cubes with complex measures
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Sliding-window filtering: an efficient algorithm for incremental mining
Proceedings of the tenth international conference on Information and knowledge management
Maintaining stream statistics over sliding windows: (extended abstract)
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Clustering Data Streams: Theory and Practice
IEEE Transactions on Knowledge and Data Engineering
Streaming-Data Algorithms for High-Quality Clustering
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Statistical grid-based clustering over data streams
ACM SIGMOD Record
Fast and Exact Out-of-Core K-Means Clustering
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
ACM SIGMOD Record
Elementary Statistics Using Excel, Second Edition
Elementary Statistics Using Excel, Second Edition
Cell trees: An adaptive synopsis structure for clustering multi-dimensional on-line data streams
Data & Knowledge Engineering
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Clustering by random projections
ICDM'07 Proceedings of the 7th industrial conference on Advances in data mining: theoretical aspects and applications
A clustering algorithm based on matrix over high dimensional data stream
WISM'10 Proceedings of the 2010 international conference on Web information systems and mining
Document clustering using synthetic cluster prototypes
Data & Knowledge Engineering
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A good clustering method should provide flexible scalability on the number of dimensions as well as the size of a data set. This paper proposes a method of efficiently tracing the clusters of a high-dimensional on-line data stream. While tracing the one-dimensional clusters of each dimension independently, a technique which is similar to frequent itemset mining is employed to find the set of multi-dimensional clusters. By finding a frequently co-occurred set of one-dimensional clusters, it is possible to trace a multi-dimensional rectangular space whose range is defined by the one-dimensional clusters collectively. In order to trace such candidates over a multi-dimensional online data stream, a cluster-statistics tree (CS-Tree) is proposed in this paper. A k-depth node(k=