基于自適應(yīng)模糊C-均值的增量式聚類算法

針對模糊C-均值(FCM)算法不能很好地處理更新數(shù)據(jù)的缺點(diǎn)，提出基于FCM 的自適應(yīng)增量式聚類算法AIFCM。該算法結(jié)合密度和集合的思想，給出一種自動確定聚類初始中心的方法，能在聚類過程中動態(tài)改變聚類結(jié)果數(shù)，改善聚類的質(zhì)量，減少人為的主觀因素，獲得比較符合用戶需求的聚類結(jié)果，并能在原有聚類結(jié)果的基礎(chǔ)上簡單有效地處理更新數(shù)據(jù)，過濾噪聲數(shù)據(jù)，較好地避免大量重復(fù)計算。
關(guān)鍵詞：聚類分析；模糊C-均值算法；增量式聚類；AIFCM 算法

【Abstract】Fuzzy C-Means(FCM) clustering algorithm can not deal with updated data, so this paper presents Incremental Clustering Algorithm
Based on Adaptive FCM namely AIFCM. It combines the density with data set, and can automatically decide the prototypes of the clusters and the
number of the clusters when the cluster is splitting. It can deal with updated data filter the outliers and satisfy with the requirement of users.
【Key words】cluster analysis; Fuzzy C-Means(FCM) algorithm; incremental clustering; Adaptive Incremental Fuzzy C-Means(AIFCM) algorithm