[1]王玉雷,李玲娟. 一种密度和划分结合的聚类算法[J].计算机技术与发展,2015,25(09):53-56.
　WANG Yu-le,LI Ling-juan. A Clustering Algorithm of Combination of Density and Division[J].,2015,25(09):53-56.
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 一种密度和划分结合的聚类算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
25
期数:
2015年09期
页码:
53-56
栏目:
智能、算法、系统工程
出版日期:
2015-09-10

文章信息/Info

Title:
 A Clustering Algorithm of Combination of Density and Division
文章编号:
1673-629X（2015)09-0053-04
作者:
 王玉雷; 李玲娟
 南京邮电大学 计算机学院
Author(s):
 WANG Yu-le; LI Ling-juan
关键词:
 ; 数据挖掘; k -means; DBSCAN; 聚类; 密度; 划分
Keywords:
 ; data mining; k -means; DBSCAN; clustering; density ; division
分类号:
TP311
文献标志码:
A
摘要:
 基于密度的DBSCAN聚类算法和基于划分的k-means聚类算法各有其优缺点。文中在k-means和DBSCAN聚类算法的基础上，以减少聚类算法对参数和数据点输入顺序的敏感性，发现任意形状的簇，提高聚类挖掘的质量为目标，提出了一种密度和划分结合的聚类算法—DDCA。该算法首先计算数据点的密度，以密度不小于给定阈值的中心点以及在其密度范围内的点组合成各个基本簇；再依据两个簇中心点之间的距离合并基本簇；最后把没有划分到任意簇的点划分到与其距离最近的簇中。理论分析和基于KDD CUP 99数据集的实验结果表明，提出的DDCA算法能够发现任意形状的簇，对数据点的输入顺序以及参数不敏感，在时间开销仅略有增加的情况下可获得更高的聚类准确度，其总体性能优于k -means。
Abstract:
 Both the density-based clustering algorithm DBSCAN and the division-based clustering algorithm k-means have their advan-tages and disadvantages. In order to reduce the sensitivity of clustering algorithm to the parameters and the input order of the data points, finding clusters of arbitrary shape and improving the quality of clustering mining,on the basis of DBSCAN and k-means clustering algo-rithm,propose a clustering algorithm combined density and division,named DDCA. This algorithm firstly calculates the density of data points,then combines the center point which has a density greater than a given threshold value and others point which in the density range of the center point to build basic clusters. Then merge two basic clusters according to the distance between their center points. Finally,di-vide point which is not belong to any cluster into its nearest cluster. Theoretical analysis and experimental results on KDD CUP 99 dataset show that this algorithm can find clusters of arbitrary shape,and is not sensitive to parameters and the input order of data points. It can get higher clustering accuracy with a little additional time cost. Its overall performance is better than k -means clustering algorithm.

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更新日期/Last Update: 2015-10-16