A Feature-Reduction Fuzzy C-Means Algorithm for Interval Data

• Main Authors: Wan-Ru Liu, 劉宛儒
• Other Authors: Miin-Shen Yang
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/db6cae

碩士 === 中原大學 === 應用數學研究所 === 106 === Fuzzy clustering algorithm usually considers equally important information for all feature components of data. In a large amount of data, unimportant messages may appear for some feature components. However, in the process of most fuzzy clustering algorithms, information are always involved in even though they are not important or relevant in the data. This may cause errors in results of fuzzy clustering algorithms. Recently, Yang and Nataliani (2017) proposed a method to improve the fuzzy c-means (FCM) algorithm, called Feature-Reduction Fuzzy C-Means (FRFCM) algorithm. The FRFCM algorithm can automatically calculate different importance of feature components. That is, it can give different importance according to the relevance of the message, and at the same time it can also reduce the related information. However, the FRFCM algorithm is only for general data, not for interval value data. We know that interval value data are also often used, such as the highest temperature and the lowest temperature of the weather, the freezing point and the boiling point of the object, etc. This thesis will expand the FRFCM algorithm to treat interval value data. We further discuss validity and practicability of the proposed clustering algorithm for interval-valued data.