Application of Bio-inspired Methods Within Cluster Forest Algorithm

被引：2

作者：

Janousek, Jan ^{[1
]}

Gajdos, Petr ^{[1
]}

Radecky, Michal ^{[1
]}

Snasel, Vaclav ^{[1
]}

机构：

[1] VSB Tech Univ Ostrava, FEECS, Dept Comp Sci, Ostrava, Czech Republic

来源：

PROCEEDINGS OF THE SECOND INTERNATIONAL AFRO-EUROPEAN CONFERENCE FOR INDUSTRIAL ADVANCEMENT (AECIA 2015) | 2016年 / 427卷

关键词：

Cluster Forest; Clustering; Ensemble clustering; Optimization; Soft computing; OPTIMIZATION;

D O I：

10.1007/978-3-319-29504-6_24

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Cluster Forest (CF) is relatively new ensemble clustering method inspired by Random Forest algorithm. The main idea behind of the existing algorithm consists in a construction of a larger number of partial clusterings for feature subsets using K-means algorithm. At the end, these clusterings are aggregated using a method of spectral clustering. This article describes a newapplication of bio-inspired methods that replaces the K-means algorithm in the computation pipeline. Several bio-inspired methods were tested on eight different datasets and compared with the original CF and others well known clustering methods.

引用

页码：237 / 247

页数：11

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