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
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Incremental Clustering for Mining in a Data Warehousing Environment
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Streaming-Data Algorithms for High-Quality Clustering
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Statistical grid-based clustering over data streams
ACM SIGMOD Record
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Incremental clustering of dynamic data streams using connectivity based representative points
Data & Knowledge Engineering
C-DenStream: Using Domain Knowledge on a Data Stream
DS '09 Proceedings of the 12th International Conference on Discovery Science
Towards subspace clustering on dynamic data: an incremental version of PreDeCon
Proceedings of the First International Workshop on Novel Data Stream Pattern Mining Techniques
Density based subspace clustering over dynamic data
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
Memory-less unsupervised clustering for data streaming by versatile ellipsoidal function
Proceedings of the 20th ACM international conference on Information and knowledge management
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The data stream model of computation is often used for analyzing huge volumes of continuously arriving data. In this paper, we present a novel algorithm called DUCstream for clustering data streams. Our work is motivated by the needs to develop a single-pass algorithm that is capable of detecting evolving clusters, and yet requires little memory and computation time. To that end, we propose an incremental clustering method based on dense units detection. Evolving clusters are identified on the basis of the dense units, which contain relatively large number of points. For efficiency reasons, a bitwise dense unit representation is introduced. Our experimental results demonstrate DUCstream's efficiency and efficacy.