An Adaptive Cluster Validity Index for the Fuzzy C-means
نویسندگان
چکیده
Based on the basic theory of fuzzy set, this paper suggests the notion of FCM fuzzy set, which is subject to the constraint condition of fuzzy c-means clustering algorithm. The cluster fuzzy degree and the lattice degree of approaching for the FCM fuzzy set are presented, and their functions in the validation process of fuzzy clustering are deeply analyzed. A new cluster validity index is proposed, in which the two factors such as the cluster fuzzy degree and the lattice degree of approaching are taken into comprehensive account. The notable advantage of the index is that it can adaptively adjust the relative significance levels of the two factors. Also, this paper gives the algorithm to apply the cluster validity index to the cluster validation for the fuzzy c-means algorithm. The experimental results indicate the effectiveness and adaptability of the proposed cluster validity index.
منابع مشابه
Intra-cluster Similarity Index Based on Fuzzy Rough Sets for Fuzzy C-Means Algorithm
Cluster validity indices have been used to evaluate the quality of fuzzy partitions. In this paper, we propose a new index, which uses concepts of Fuzzy Rough sets to evaluate the average intra-cluster similarity of fuzzy clusters produced by the fuzzy c-means algorithm. Experimental results show that contrasted with several well-known cluster validity indices, the proposed index can yield more...
متن کاملAssessing the Quality of Fuzzy Partitions Using Relative Intersection
In this paper, conventional validity indexes are reviewed and the shortcomings of the fuzzy cluster validation index based on intercluster proximity are examined. Based on these considerations, a new cluster validity index is proposed for fuzzy partitions obtained from the fuzzy c-means algorithm. The proposed validity index is defined as the average value of the relative intersections of all p...
متن کاملFuzzy Cluster Validity with Generalized Silhouettes
A review of some popular fuzzy cluster validity indices is given. An index that is based on the generalization of silhouettes to fuzzy partitions is compared with the reviewed indices in conjunction with fuzzy c-means clustering.
متن کاملA Self-Adaptive Fuzzy c-Means Algorithm for Determining the Optimal Number of Clusters
For the shortcoming of fuzzy c-means algorithm (FCM) needing to know the number of clusters in advance, this paper proposed a new self-adaptive method to determine the optimal number of clusters. Firstly, a density-based algorithm was put forward. The algorithm, according to the characteristics of the dataset, automatically determined the possible maximum number of clusters instead of using the...
متن کاملMulti-Output Adaptive Neuro-Fuzzy Inference System for Prediction of Dissolved Metal Levels in Acid Rock Drainage: a Case Study
Pyrite oxidation, Acid Rock Drainage (ARD) generation, and associated release and transport of toxic metals are a major environmental concern for the mining industry. Estimation of the metal loading in ARD is a major task in developing an appropriate remediation strategy. In this study, an expert system, the Multi-Output Adaptive Neuro-Fuzzy Inference System (MANFIS), was used for estimation of...
متن کامل