一種基于GA的混合屬性特征大數(shù)據(jù)集聚類算法
A GA-Based Clustering Algorithm for Large Data Sets with Mixed Numerical and Categorical Values
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摘要: 在數(shù)據(jù)挖掘中，經(jīng)常會遇到和分析大量具有數(shù)值和類屬特征的數(shù)據(jù)。然而，現(xiàn)有的大多數(shù)算法只能單獨處理數(shù)值特征數(shù)據(jù)或類屬特征數(shù)據(jù)，而不能分析具有混合屬性的數(shù)據(jù)。為此，該文提出了一種基于GA的模糊聚類新算法，通過改進(jìn)聚類目標(biāo)函數(shù)將數(shù)值特征與類屬特征相結(jié)合，從而實現(xiàn)具有混合屬性特征數(shù)據(jù)的聚類分析；通過引入GA算法能夠快速得到全局最優(yōu)解，而且不依賴于原型初始化。實驗結(jié)果表明，基于GA的新聚類算法對于處理具有混合特征的大數(shù)據(jù)集聚類問題是相當(dāng)有效的。
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關(guān)鍵詞:
- 聚類分析; 數(shù)值特征; 類屬特征; 遺傳算法
Abstract: In the field of data mining, it is often encountered to perform cluster analysis on large data sets with mixed numerical and categorical values. However, most existing clustering algorithms are only efficient for the numerical 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 Genetic Algorithm (GA) is used to optimize the new cost function to obtain valid clustering result. Experimental result illustrates that the GA-based new clustering algorithm is feasible for the large data sets with mixed numerical and categorical values. -
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