Maximum entropy-type classification likelihood methods

• Main Authors: Jing-Tang Juang, 莊景棠
• Other Authors: Miin-Shen Yang
• Format: Others
• Language: zh-TW
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/65073862497665161959

碩士 === 中原大學 === 應用數學研究所 === 95 === In fuzzy cluster analysis , the fuzzy c-means (FCM) clustering algorithm is the best known and most used method. There are many generalized types of FCM. Some of them such as fuzzy classification maximum likelihood (FCML) induce to penalized fuzzy c-means (PFCM) , Maximum entropy classification (MEC) and alternative fuzzy c-means (AFCM) , will be studied in this thesis, then we can get better results. In this paper , we make the extension of the FCM , based on this class of fuzzy c-means clustering algorithm , we extend them by adding a regularization , the regularization is change by membership , we can derive a generalized type of fuzzy c-means clustering algorithms , called the maximum entropy clustering algorithm (MEC). By doing some numerical examples , for estimating the parameters of the normal mixtures , we find that MEC is more accuracy and effective then PFCM and FCM.