A CSA-Based Clustering Algorithm for Large Data Sets with Mixed Numeric and Categorical Values

LI Jie; GAO Xin-bo; JIAO Li-cheng

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A CSA-Based Clustering Algorithm for Large Data Sets with Mixed Numeric and Categorical Values

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- A CSA-Based Clustering Algorithm for Large Data Sets with Mixed Numeric and Categorical Values
- Acta Electronica Sinica Vol. 32, Issue 3, Pages: 357-362(2004)
- 作者机构：
  
  西安电子科技大学电子工程学院,陕西,西安,710071
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP391
- Published：2004
- 稿件说明：
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LI Jie, GAO Xin-bo, JIAO Li-cheng. A CSA-Based Clustering Algorithm for Large Data Sets with Mixed Numeric and Categorical Values[J]. Acta Electronica Sinica, 2004, 32(3): 357-362.
DOI：

LI Jie, GAO Xin-bo, JIAO Li-cheng. A CSA-Based Clustering Algorithm for Large Data Sets with Mixed Numeric and Categorical Values[J]. Acta Electronica Sinica, 2004, 32(3): 357-362. DOI：

摘要

在数据挖掘中

我们经常会遇到和分析大量具有数值和类属特征的数据.然而

现有的大多数分类算法只能单独处理数值特征数据或类属特征数据

而不能分析具有两种混合属性的数据.为此

本文提出一种基于克隆选择的模糊聚类新算法

通过改进距离测度函数将数值特征与类属特征相结合

从而实现具有混合属性特征数据的聚类分析;通过引入克隆选择算法(CSA)实现目标函数的全局优化.由于克隆算子能够将进化搜索与随机搜索、全局搜索和局部搜索相结合

因而通过对候选解进行克隆算子操作

能够快速得到全局最优解.实验结果表明

基于CSA的模糊聚类新算法对于处理具有混和特征的大数据集聚类问题是相当有效的.

Abstract

In the field of data mining

it is often encountered to perform cluster analysis on large data sets with mixed numeric and categorical values.However

most existing clustering algorithms are only efficient for the numeric data rather than the mixed data set.For this purpose

this paper presents a novel clustering algorithm for these mixed data sets by modifying the common cost function

trace of the within cluster dispersion matrix.The clonal selection algorithm (CSA) is used to optimize the new cost function

since the clonal operator can combine the evolutionary search and random search

and incorporate the global search with local search

by the clonal operation on candidate solutions;the new algorithm can quickly obtain the global optimum.Experimental result illustrates that the CSA-based new clustering algorithm is feasible for the large data sets with mixed numeric and categorical values.

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