CAJ | 학술논문

针对传统基于最大熵模糊 C 均值聚类算法(MEFCM)仅适用于球状或椭圆状聚类，为了解决数据分布混乱以及高度相关难以划分的情形，引入 Mercer 核函数，使原来没有显现的特征突现出来，从而使聚类效果更好。然而在实际问题中，大多数样本集的样本数据都存在着重要性(权重)不同的现象，主要针对样本集中各个数据的不同重要程度来设计加权方法，同时为了克服聚类算法对初始聚类中心选取的敏感性这一弱点，提出了一个初始聚类中心优化的加权最大熵核模糊聚类算法(WKMEFCM)。通过实验验证，该算法与原MEFCM算法比较，其聚类结果更加稳定、准确，从而达到更好的聚类划分效果。
침대전통기우최대적모호 C 균치취류산법(MEFCM)부괄용우구상혹타원상취류，위료해결수거분포혼란이급고도상관난이화분적정형，인입 Mercer 핵함수，사원래몰유현현적특정돌현출래，종이사취류효과경호。연이재실제문제중，대다수양본집적양본수거도존재착중요성(권중)불동적현상，주요침대양본집중각개수거적불동중요정도래설계가권방법，동시위료극복취류산법대초시취류중심선취적민감성저일약점，제출료일개초시취류중심우화적가권최대적핵모호취류산법(WKMEFCM)。통과실험험증，해산법여원MEFCM산법비교，기취류결과경가은정、준학，종이체도경호적취류화분효과。
This paper aims to demonstrate the traditional maximum entropy fuzzy C-means clustering algorithm (MEFCM) applies to spherical or oval-shaped clusters only. In order to solve the confusion and highly relevant data distribution division of this difficult situation, it introduces Mercer kernel function, so that the original features which do not show can stand out and make the clustering effect better. However, in practical, the majority of sample sets are exist importance (weighting) of different phenomena. The main focus are the samples of different importance to design of each data weighting and in order to overcome the sensitivity of weakness of the initial cluster centers. This paper presents an optimization of the initial cluster centers weighted kernel maximum entropy fuzzy clustering algorithm (WKMEFCM). Experiments show that compared with the MEFCM, the clustering result is more stable, accurate and the clusters division effect is better.