In the field of information retrieval, clustering algorithms are used to analyze large collections of documents with the objective to form groups of similar documents. Clustering a document collection is an ambiguous task: A clustering, i. e. a set of document groups, depends on the chosen clustering algorithm as well as on the algorithm’s parameter settings. To find the best among several clus...

In this paper, an evolutionary clustering technique is described that uses a point symmetry based distance measure. The algorithm is therefore able to detect both convex and non-convex clusters. Kd-tree based nearest neighbor search is used to reduce the complexity of finding the closest symmetric point. The proposed GA with point symmetry distance based (GAPS) clustering technique is compared ...

A new clustering algorithm based on grid projections is proposed. This algorithm, called U*C, uses distance information together with density structures. The number of clusters is determined automatically. The validity of the clusters found can be judged by the U*-Matrix visualization on top of the grid. A U*-Matrix gives a combined visualization of distance and density structures of a high dim...

Text documents have sparse data spaces and current existing methods of text clustering use symmetry proximity to measure the correlation of documents. In this paper, we propose a novel approach to strengthen the discriminative feature of document objects, which uses asymmetric proximity for text clustering. We present a measure of asymmetric proximity between documents and between clusters. TCU...

A simple and effective fuzzy clustering approach is presented for fuzzy modeling from industrial data. In this approach, fuzzy clustering is implemented in two phases: data compression by a self-organizing network, and fuzzy partitioning via fuzzy cmeans clustering associated with a proposed cluster validity measure. The approach is used to extract fuzzy models from data and find out the optima...

Although the validation step can appear crucial in the case of clustering adopting fuzzy approaches, the problem of the partition validity obtained by those adopting the hard ones was not tackled. To cure this problem, we propose in this paper fuzzy-hard mapping processes of clustering while benefitting from those adopting the fuzzy case. These mapping processes concern: (1) local and global cl...

Gaining confidence that a clustering algorithm has produced meaningful results and not an accident of its usually heuristic optimization is central to data analysis. This is the issue of validity and we propose here a method by which Support Vector Machines are used to evaluate the separation in the clustering results. However, we not only obtain a method to compare clustering results from diff...

K-Means clustering is a well-known tool in unsupervised learning. The performance of K-Means clustering, measured by the F-ratio validity index, highly depends on selection of its initial partition. This problematic dependency always leads to a local optimal solution for k-center clustering. To overcome this difficulty, we present an intuitive approach that iteratively incorporates Fisher discr...