模式识别与人工智能
模式識彆與人工智能
모식식별여인공지능
Moshi Shibie yu Rengong Zhineng
2010年
1期
11-16
,共6页
模糊C均值聚类%有效性函数%类内不一致性%类间重叠度
模糊C均值聚類%有效性函數%類內不一緻性%類間重疊度
모호C균치취류%유효성함수%류내불일치성%류간중첩도
Fuzzy C-Means Clustering%Validity Index%Intra-Class Non-Consistency%Inter-Class Overlapping
聚类的错误主要表现为两种形式:将原属不同类的数据分到同一个聚类和将原属同一类的数据分到不同聚类.文中提出类内不一致性和类间重叠度两个指标分别度量聚类中出现这两类错误的程度.一个好的模糊分割中包含的聚类错误应尽可能少.同时,聚类紧致度应尽可能大.基于这两个错误度量指标和紧致性度量,提出一种有效性函数来判断模糊聚类的有效性.实验结果表明,提出的有效性函数能有效判断最佳聚类数并且有较好的鲁棒性.
聚類的錯誤主要錶現為兩種形式:將原屬不同類的數據分到同一箇聚類和將原屬同一類的數據分到不同聚類.文中提齣類內不一緻性和類間重疊度兩箇指標分彆度量聚類中齣現這兩類錯誤的程度.一箇好的模糊分割中包含的聚類錯誤應儘可能少.同時,聚類緊緻度應儘可能大.基于這兩箇錯誤度量指標和緊緻性度量,提齣一種有效性函數來判斷模糊聚類的有效性.實驗結果錶明,提齣的有效性函數能有效判斷最佳聚類數併且有較好的魯棒性.
취류적착오주요표현위량충형식:장원속불동류적수거분도동일개취류화장원속동일류적수거분도불동취류.문중제출류내불일치성화류간중첩도량개지표분별도량취류중출현저량류착오적정도.일개호적모호분할중포함적취류착오응진가능소.동시,취류긴치도응진가능대.기우저량개착오도량지표화긴치성도량,제출일충유효성함수래판단모호취류적유효성.실험결과표명,제출적유효성함수능유효판단최가취류수병차유교호적로봉성.
The mistakes in fuzzy clustering can be categorized into two types:classifying data originated from different classes into one cluster and classifying data originated from the same class into different clusters.In this paper,intra-class non-consistency and inter-class overlapping are defined to measure the two kinds of mistakes respectively.A good fuzzy partition is expected to have few clustering mistakes and large compactness.Based on the two mistake measures and cluster compactness,a cluster validity index is proposed to evaluate the clustering results.Experimental results show the effectiveness and the robustness of the proposed validity index in determining optimal number of clusters.
