一种新的混合聚类算法

聚类是数据挖掘的主要技术之一,是一种无导师监督的模式识别方式。聚类分析就是按照数据间的相似程度,依据特定的准则将数据划分成不同子类。K-平均算法是经典的聚类算法。蚂蚁聚类算法是近来涌现的新的聚类算法,它通过模拟蚁群的智能行为进行聚类分析,已经在数据挖掘中得到应用。通过分析蚂蚁聚类算法和K-平均算法两种不同聚类算法的基本思想,将两种算法结合得到混合聚类算法,仿真实验证明混合聚类算法的算法性能优于蚂蚁算法和K-平均算法。

New Mixed Clustering Algorithm

Clustering is one of primary techniques in the filed of data mining.It is an unsupervised mode of pattern recognition.Clustering analysis is a division of data into similarity groups according to given rules.K-means algorithm is a classical clustering algorithm.Ant-clustering-algorithm which simulates intelligence actions of ant colony has been applied in the data mining.In this paper,a new mixed clustering algorithm was proposed which combined k-means algorithm with ant-clustering-algorithm on the base of the fundamental principle about two methods.The results of experiment demonstrate that this new mixed clustering algorithm has the advantage over the two others in performance.