A Study on Generalized Fuzzy C-means Algorithms

• Main Authors: Jia-Shiuan Chang, 張嘉軒
• Other Authors: Miin-Shen Yang
• Format: Others
• Language: zh-TW
• Published: 2004
• Online Access: http://ndltd.ncl.edu.tw/handle/67662112342801878834

碩士 === 中原大學 === 應用數學研究所 === 92 === In 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 the conditional fuzzy c-means (CFCM), alternative fuzzy c-means (AFCM), penalized fuzzy c-means (PFCM), partition index maximization (PIM), inter-cluster separation (ICS), maximum entropy-based clustering (MEC) and fuzzy generalized c-means (FGcM) will be studied in this thesis. In fact, these algorithms can be thought of a generalized FCM (GFCM). We proposed a new algorithm based on GFCM. We add a penalty term to the ICS and then extend the ICS to the so-called penalized ICS (PICS). Described here are five approaches for estimating the parameters of a mixture of normal distributions. These are FCM, PFCM, PIM, ICS, and PICS clustering algorithms. The accuracy and computational efficiency of these five types of algorithms for estimating the parameters of the normal mixtures are compared using samples drawn from some univariate normal mixtures of two classes.