In some complicated datasets, due to the presence of noisy data points and outliers, cluster validity indices can give conflicting results in determining optimal number clusters. This paper presents a new index for fuzzy-possibilistic c-means clustering called Fuzzy-Possibilistic)FP (index, which works well clusters that vary shape density. Moreover, FPCM like most algorithms is susceptible ini...

The goal of any clustering algorithm is to find the optimal clustering solution with the optimal number of clusters. In order to evaluate a clustering solution, a number of validity indices are used during or at the end of a clustering process. They can be internal, external or relative. In this paper, we provide two main contributions: First, we present an experimental study comparing the majo...

Cluster validity indexes have been used to evaluate the fitness of partitions produced by clustering algorithms. This paper presents a new validity index for fuzzy clustering called a partition coefficient and exponential separation (PCAES) index. It uses the factors from a normalized partition coefficient and an exponential separation measure for each cluster and then pools these two factors t...

Clustering is a mostly unsupervised procedure and the majority of the clustering algorithms depend on certain assumptions in order to define the subgroups present in a data set. Moreover, they may behave in a different way depending on the features of the data set and their input parameters values. Therefore, in most applications the resulting clustering scheme requires some sort of evaluation ...

Many validity measures have been proposed for evaluating clustering results. Most of these popular validity measures do not work well for clusters with different densities and/or sizes. They usually have a tendency of ignoring clusters with low densities. In this paper, we propose a new validity measure, which can deal with this situation. The performance evaluation of the validity measure comp...

When clustering algorithms are applied to image seg-mentation, the goal is to solve a classification problem. However, these algorithms do not directly optimize classification quality. As a result, they are susceptible to two problems: P1) the criterion they optimize may not be a good estimator of " true " classification quality, and P2) they often admit many (suboptha€) solutions. This paper i...

Much attention is being given to the incorporation of constraints into data clustering, mainly expressed in the form of must-link and cannot-link constraints between pairs of domain objects. However, its inclusion in the important clustering validation process was so far disregarded. In this work, we integrate the use of constraints in clustering validation. We propose three approaches to accom...