In this paper, we propose a new method to specify the sequence of parameter values for a fuzzy clustering algorithm by using Q-learning. In the clustering algorithm, we employ similarities between two data points and distances from data to cluster centers as the fuzzy clustering criteria. The fuzzy clustering is achieved by optimizing an objective function which is solved by the Picard iteratio...

We consider the stability of k-means clustering problems. Clustering stability is a common heuristics used to determine the number of clusters in a wide variety of clustering applications. We continue the theoretical analysis of clustering stability by establishing a complete characterization of clustering stability in terms of the number of optimal solutions to the clustering optimization prob...

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...

Ensemble methods are known to increase the performance of learning algorithms, both on supervised and unsupervised learning. Boosting algorithms are quite successful in supervised ensemble methods. These algorithms build incrementally an ensemble of classifiers by focusing on objects previously misclassified while training the current classifier. In this paper we propose an extension to the Evi...

In this paper, a new clustering method is applied to segmentation and volume change assessment in lateral ventricles affected by hydrocephalus pathology in the infants. The hydrocephalus is characterized as such, which results from excessive accumulation of cerebrospinal fluid in the ventricles, leading to enlargement of cerebral ventricles. The complex shape o the ventricular system is evaluat...

Data mining isa process of extracting interested hidden information from large databases. It can be applied on many databases but kind of patterns to be found is specified by various data mining techniques.Clustering is one of the data mining techniques that partitions database into clusters such that data objects in same clusters are similar and data objects belonging to different cluster are ...

Some major problems in clustering are: i) find the optimal number K of clusters; ii) assess the validity of a given clustering; iii) permit the classes to form natural shapes rather than forcing them into normed balls of the distance function; iv) prevent the order in which the feature vectors are read in from affecting the clustering; and v) prevent the order of merging from affecting the clus...