A novel information identification model is proposed to support accurate classification tasks with mixtures of categorical and real-valued attributes. This model combines the advantages of rough set theory and cluster validity method to promote the classification quality to the higher levels. Real-valued attribute values are pre-processed by fuzzy c-means clustering method and then analyzed by ...

The intent of this article is to illustrate how a cluster analysis might be conducted, validated, and interpreted. Data normed for a behavioral assessment instrument with 14 scales on a sample drawn from a nationally representative pool of U.S. school children were utilized. The analysis discussed covers the cluster method, cluster typology, cluster validity, cluster structure, and prediction o...

Cluster validation is a major issue in cluster analysis. Many existing validity indices do not perform well when clusters overlap or there is significant variation in their covariance structure. The contribution of this paper is twofold. First, we propose a new validity index for fuzzy clustering. Second, we present a new approach for the objective evaluation of validity indices and clustering ...

Validating a given clustering result is a very challenging task in real world. So for this purpose, several cluster validity indices have been developed in the literature. Cluster validity indices are divided into two main categories: external and internal. External cluster validity indices rely on some supervised information available and internal validity indices utilize the intrinsic structu...

The k-means method has been shown to be effective in producing good clustering results for many practical applications. However, a direct algorithm of k-means method requires time proportional to the product of number of patterns and number of clusters per iteration. This is computationally very expensive especially for large datasets. The main disadvantage of the k-means algorithm is that the ...

The validation of the results obtained by clustering algorithms is a fundamental part of the clustering process. The most used approaches for cluster validation are based on internal cluster validity indices. Although many indices have been proposed, there is no recent extensive comparative study of their performance. In this paper we show the results of an experimental work that compares 30 cl...

We develop a three-step fuzzy logic-based algorithm for clustering categorical attributes, and we apply it to analyze cultural data. In the first step the algorithm employs an entropy-based clustering scheme, which initializes the cluster centers. In the second step we apply the fuzzy c-modes algorithm to obtain a fuzzy partition of the data set, and the third step introduces a novel cluster va...

The evaluation and comparison of internal cluster validity indices is a critical problem in the clustering area. The methodology used in most of the evaluations assumes that the clustering algorithms work correctly. We propose an alternative methodology that does not make this often false assumption. We compared 7 internal cluster validity indices with both methodologies and concluded that the ...