A Fuzzy Weighted Sum Validity Function for Clustering with a Mixed Strategy Evolutionary Algorithm

DONG Hong-bin; HUANG Hou-kuan; ZHOU Cheng; HE Jun; SHANG Wen-qian

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A Fuzzy Weighted Sum Validity Function for Clustering with a Mixed Strategy Evolutionary Algorithm

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- A Fuzzy Weighted Sum Validity Function for Clustering with a Mixed Strategy Evolutionary Algorithm
- Acta Electronica Sinica Vol. 35, Issue 5, Pages: 964-970(2007)
- 作者机构：
  
  1. 北京交通大学计算机与信息技术学院,北京,100044
  2. 哈尔滨师范大学计算机科学系,黑龙江,哈尔滨,150080
  3. 北京交通大学计算机与信息技术学院北京,100044
  4. 哈尔滨师范大学计算机科学系黑龙江哈尔滨,150080
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP18
- Published：2007
- 稿件说明：
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DONG Hong-bin, HUANG Hou-kuan, ZHOU Cheng, et al. A Fuzzy Weighted Sum Validity Function for Clustering with a Mixed Strategy Evolutionary Algorithm[J]. Acta Electronica Sinica, 2007, 35(5): 964-970.
DOI：

DONG Hong-bin, HUANG Hou-kuan, ZHOU Cheng, et al. A Fuzzy Weighted Sum Validity Function for Clustering with a Mixed Strategy Evolutionary Algorithm[J]. Acta Electronica Sinica, 2007, 35(5): 964-970. DOI：

摘要

本文改进了Sheng的权和有效性函数

将XB、PE、PC和PBMF等模糊聚类有效性函数集成为一种新的模糊聚类有效性度量函数—模糊权和有效性函数FWSVF

从而提高了聚类有效性函数的性能.为了有效的实现聚类

将混合策略演化算法与传统的模糊C均值算法(FCM)相结合

将改进的模糊权和有效性指标作为适应度函数

提出了一种混合策略演化聚类算法MSECA.人工数据集和真实数据集的仿真实验表明

MSECA算法可以正确发现聚类簇的数量

避免了局部极值问题

比其他算法具有更好的性能.

Abstract

Clustering is inherently a difficult problem

with respect to both construction of adequate objective functions and optimization of the objective functions.In this paper

we propose a novel objective function called the Fuzzy Weighted Sum Validity Function (FWSVF)

which is a merged weight from the several fuzzy cluster validity functions

including XB

PC and PBMF.The new validity function has more efficient quality than old ones.Furthermore

we present a Mixed Strategy Evolutionary Clustering Algorithm (MSECA)

which is merged from Mixed Strategy Evolutionary Algorithm and Fuzzy C-means Algorithm and could be applied to optimization of FWSVF. The improved validity function could improve the confidence of clustering solutions and achieve more accurate and robust results.Moreover

MSECA could automatically evolve the proper number of clusters as well as appropriate partitions of the data set

and avoid local optimum.In the experiments

we show the effectiveness of MSECA.In comparison with other genetic clustering algorithms

the MSECA can consistently and efficiently converge to the best known optimum corresponding to given data in concurrence with the convergence result.

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