Clustering accuracy of partitional clustering algorithm for categorical data depends primarily on the choice of initial data points to instigate the clustering process and hence the clustering results cannot be generated and repeated consistently. In this paper we present an approach to compute initial modes for K-mode partitional clustering algorithm to cluster categorical data sets. Here we u...

Clustering is an important tool to explore the hidden structure of large databases. There are several algorithms based on different approaches (hierarchical, partitional, density-based, model-based, etc.). Most of these algorithms have some discrepancies, e.g. they are not able to detect clusters with convex shapes, the number of the clusters should be a priori known, they suffer from numerical...

There are large quantities of information about patients and their medical conditions. The discovery of trends and patterns hidden within the data could significantly enhance understanding of disease and medicine progression and management by evaluating stored medical documents. Methods are needed to facilitate discovering the trends and patterns within such large quantities of medical document...

We have carried out experiments in clustering a news corpus. In these experiments we have used two partitional methods varying two different parameters of the clustering tool. In addition, we have worked with the whole document (news) and with representative parts of the document. We have obtained good results working with a representative part of the document. The experiments have been carried...

Data clustering is a recognized data analysis method in data mining whereas K-Means is the well known partitional clustering method, possessing pleasant features. We observed that, K-Means and other partitional clustering techniques suffer from several limitations such as initial cluster centre selection, preknowledge of number of clusters, dead unit problem, multiple cluster membership and pre...

This paper describes a novel method aiming to cluster datasets containing malware behavioural data. Our method transform the data into an standardised data matrix that can be used in any clustering algorithm, finds the number of clusters in the data set and includes an optional visualization step for high-dimensional data using principal component analysis. Our clustering method deals well with...

Clustering is a powerful tool in revealing the intrinsic organization of data. A clustering of structural patterns consists of an unsupervised association of data based on the similarity of their structures and primitives. This chapter addresses the problem of structural clustering, and presents an overview of similarity measures used in this context. The distinction between string matching and...