A novel fuzzy-connectedness-based incremental clustering algorithm for large databases

Authors:
Yihong Dong;Xiaoying Tai;Jieyu Zhao
Affiliations:
Institute of Computer Science and Technology, Ningbo University, Ningbo, China;Institute of Computer Science and Technology, Ningbo University, Ningbo, China;Institute of Computer Science and Technology, Ningbo University, Ningbo, China
Venue:
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
Year:
2005

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Incremental Clustering for Mining in a Data Warehousing Environment

VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases

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Abstract

Many clustering methods have been proposed in data mining fields, but seldom were focused on the incremental databases. In this paper, we present an incremental algorithm-IFHC that is applicable in periodically incremental environment based on FHC[3]. Not only can FHC and IFHC dispose the data with numeric attributes, but with categorical attributes. Experiment shows that IFHC is faster and more efficient than FHC in update of databases.