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

被引：0
|
作者：
Dong, YH [1 ]
Tai, XY
Zhao, JY
机构：
[1] Ningbo Univ, Inst Comp Sci & Technol, Ningbo 315211, Peoples R China
[2] Zhejiang Univ, Inst Artificial Intelligence, Hangzhou 310027, Peoples R China
来源：
FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, PT 1, PROCEEDINGS | 2005年 / 3613卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：470 / 474
页数：5
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