Outlier detection for high dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Clustering by pattern similarity in large data sets
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
A mixture model for contextual text mining
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
A framework for projected clustering of high dimensional data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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In actual databases, there are a lot of hierarchy coding data, existing clustering algorithms don't consider the special treatment of these data structure, so lead nonideal performance and clustering result. This paper proposes a new clustering algorithm to deal with the hierarchy coding data structure (HCDS) that exists in many applications. The main contributions include: (1) proposes a new concept for HCDS and corresponding definitions. (2) Proposes and implements a new clustering algorithm---CHCC (Coding Hierarchy Computing Based Clustering Algorithm) based on HCDS. (3) Proposes a fast algorithm for hierarchy coding structure processing. (4) Applies the algorithm into the clustering analysis of transient population for public security, and through extensive experiments, proves the validity and efficiency of the algorithm.