원문정보
초록
영어
An improved fuzzy c-means algorithm is put forward and applied to deal with meteorological data on top of the traditional fuzzy c-means algorithm. The proposed algorithm improves the classical fuzzy c-means algorithm (FCM) by adopting a novel strategy for selecting the initial cluster centers, to solve the problem that the traditional fuzzy c-means (FCM) clustering algorithm has difficulty in selecting the initial cluster centers. Furthermore, this paper introduces the features and the mining process of the open source data mining platform WEKA, while it doesn’t implement the FCM algorithm. Considering this shortcoming of WEKA, we successfully implement the FCM algorithm and the advanced FCM algorithm taking advantage of the basic classes in WEKA. Finally, the experimental clustering results of meteorological data are given, which can exactly prove that our proposed algorithm will generate better clustering results than those of the K-Means algorithm and the traditional FCM algorithm.
목차
1. Introduction
2. Related Work
2.1 WEKA
2.2 The Fuzzy c-means Algorithm
3. FCM in WEKA
3.1 Theory on the FCM Algorithm
3.2 Basic Core WEKA Classes used in Clustering
3.3 The Implement Classes of the FCM Algorithm
4. The Improved FCM Algorithm for Meteorological Data
5. Experiment and Discussion
5.1 Data Description
5.2 Data Preprocessing
5.3 Clustering Analysis
6. Conclusions and Future Research
Acknowledgements
References