微型机与应用
微型機與應用
미형궤여응용
MICROCOMPUTER & ITS APPLICATIONS
2012年
20期
74-76
,共3页
K-means%聚类算法%初始聚类中心%动态聚类
K-means%聚類算法%初始聚類中心%動態聚類
K-means%취류산법%초시취류중심%동태취류
K-means%clustering algorithm%initial clustering centers%dynamic clustering
针对初始聚类中心对传统K-means算法的聚类结果有较大影响的问题，提出一种依据样本点类内距离动态调整中心点类间距离的初始聚类中心选取方法，由此得到的初始聚类中心点尽可能分散且具代表性，能有效避免K-means算法陷入局部最优。通过UCI数据集上的数据对改进算法进行实验，结果表明改进的算法提高了聚类的准确性。
針對初始聚類中心對傳統K-means算法的聚類結果有較大影響的問題，提齣一種依據樣本點類內距離動態調整中心點類間距離的初始聚類中心選取方法，由此得到的初始聚類中心點儘可能分散且具代錶性，能有效避免K-means算法陷入跼部最優。通過UCI數據集上的數據對改進算法進行實驗，結果錶明改進的算法提高瞭聚類的準確性。
침대초시취류중심대전통K-means산법적취류결과유교대영향적문제，제출일충의거양본점류내거리동태조정중심점류간거리적초시취류중심선취방법，유차득도적초시취류중심점진가능분산차구대표성，능유효피면K-means산법함입국부최우。통과UCI수거집상적수거대개진산법진행실험，결과표명개진적산법제고료취류적준학성。
There are great impacts on traditional K-means algorithm results of clustering for initial cluster centers. A new im- proved K-means algorithm is proposed. A new method for selecting initial cluster centers according to the inner class distance of samples which dynamically adjust the distance between clustering. It not only can nake the cluster centers as dispersed as possible and highly representative ,but can avoid K-means algorithm into local optimum effectively. The improved algorithm is done experi- ments on data of UCI data set, the results show that improved algorithm can improve the clustering accuracy.
