Clustering is an unsupervised technique dealing with problems of organizing a collection of patterns into clusters based on similarity. Most clustering algorithms are based on hierarchical and partitional approaches. Algorithms based on an hierarchical approach generate a dendrogram representing the nested grouping of patterns and similarity levels at which groupings change [19]. Partitional cl...

Data clustering is the concept of forming predefined number of clusters where the data points within each cluster are very similar to each other and the data points between clusters are dissimilar to each other. The concept of clustering is widely used in various domains like bioinformatics, medical data, imaging, marketing study and crime analysis. The popular types of clustering techniques ar...

The concept of Data Clustering is considered to be very significant in various application areas like text mining, fraud detection, health care, image processing, bioinformatics etc. Due to its application in a variety of domains, various techniques are presented by many research domains in the literature. Data Clustering is one of the important tasks that make up Data Mining. Clustering can be...

Clustering uncertain data has emerged as a challenging task in uncertain data management and mining. Thanks to a computational complexity advantage over other clustering paradigms, partitional clustering has been particularly studied and a number of algorithms have been developed. While existing proposals differ mainly in the notions of cluster centroid and clustering objective function, little...

Fast and high-quality document clustering algorithms play an important role in providing intuitive navigation and browsing mechanisms by organizing large amounts of information into a small number of meaningful clusters, and in greatly improving the retrieval performance either via cluster-driven dimensionality reduction, term-weighting, or query expansion. This ever-increasing importance of do...

In this paper, we analyze some widely employed clustering algorithms to identify duplicated or cloned pages in web applications. Indeed, we consider an agglomerative hierarchical clustering algorithm, a divisive clustering algorithm, k-means partitional clustering algorithm, and a partitional competitive clustering algorithm, namely Winner Takes All (WTA). All the clustering algorithms take as ...

Hierarchical clustering of text collections is a key problem in document management and retrieval. In partitional hierarchical clustering, which is more efficient than its agglomerative counterpart, the entire collection is split into clusters and the individual clusters are further split until a heuristically-motivated termination criterion is met. In this paper, we define the BIC-means algori...

Recently, a considerable growth of interest in using Nonnegative Matrix Factorization (NMF) for pattern classification and data clustering has been observed. For nonnegative data (observations, data items, feature vectors) many problems of partitional clustering can be modeled in terms of a matrix factorization into two groups of vectors: the nonnegative centroid vectors and the binary vectors ...

Text Clustering is a text mining technique which divides the given set of text documents into significant clusters. It is used for organizing a huge number of text documents into a well-organized form. In the majority of the clustering algorithms, the number of clusters must be specified apriori, which is a drawback of these algorithms. The aim of this paper is to show experimentally how to det...