Rough self organizing map

被引：25

作者：

Pal, SK ^{[1
]}

Dasgupta, B ^{[1
]}

Mitra, P ^{[1
]}

机构：

[1] Indian Stat Inst, Machine Intelligence Unit, Kolkata 700108, India

来源：

APPLIED INTELLIGENCE | 2004年 / 21卷 / 03期

关键词：

soft computing; pattern recognition; self organization; rough sets; fuzzy sets; data mining; case generation;

D O I：

10.1023/B:APIN.0000043561.99513.69

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 [模式识别与智能系统]; 0812 [计算机科学与技术]; 0835 [软件工程]; 1405 [智能科学与技术];

摘要：

A rough self-organizing map (RSOM) with fuzzy discretization of feature space is described here. Discernibility reducts obtained using rough set theory are used to extract domain knowledge in an unsupervised framework. Reducts are then used to determine the initial weights of the network, which are further refined using competitive learning. Superiority of this network in terms of quality of clusters, learning time and representation of data is demonstrated quantitatively through experiments over the conventional SOM.

引用

页码：289 / 299

页数：11

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