-Since clustering is applied in many fields, a number of clustering techniques and algorithms have been proposed and are available in the literature. This paper proposes a novel approach to address the major problems in any of the partitional clustering algorithms like choosing appropriate K-value and selection of K-initial seed points. The performance of any partitional clustering algorithms d...

Fuzzy partitional clustering algorithms are widely used in pattern recognition field. Until now, more and more research results on them have been developed in the literature. In order to study these algorithms systematically and deeply, they are reviewed in this paper based on c-means algorithm, from metrics, entropy, and constraints on membership function or cluster centers. Moreover, the adva...

Content-Based Image Retrieval (CBIR), is mainly based on finding images of interest from a large image database using the visual content of the images. Most of the approaches to image retrieval were text-based, where individual images had to be annotated with format. Existing works are based on the performance of a number of clustering algorithms in image retrieval has been analyzed. The propos...

There is a need to organize a large set of documents into categories through clustering so as to facilitate searching and finding the relevant information on the web with large number of documents becomes easier and quicker. Hence we need more efficient clustering algorithms for organizing documents. Clustering on large text dataset can be effectively done using partitional clustering algorithm...

traditional leveraging statistical methods for analyzing today’s large volumes of spatial data have high computational burdens. to eliminate the deficiency, relatively modern data mining techniques have been recently applied in different spatial analysis tasks with the purpose of autonomous knowledge extraction from high-volume spatial data. fortunately, geospatial data is considered a proper s...

In this paper we address the problem of building object class representations based on local features and fast matching in a large database. We propose an efficient algorithm for hierarchical agglomerative clustering. We examine different agglomerative and partitional clustering strategies and compare the quality of obtained clusters. Our combination of partitional-agglomerative clustering give...

In the field of pattern recognition, combining different classifiers into a robust classifier is a common approach for improving classification accuracy. Recently, this trend has also been used to improve clustering performance especially in non-hierarchical clustering approaches. Generally hierarchical clustering is preferred in comparison with the partitional clustering for applications when ...

Clustering algorithms are routinely used in biomedical disciplines, and are a basic tool in bioinformatics. Depending on the task at hand, there are two most popular options, the central partitional techniques and the Agglomerative Hierarchical Clustering techniques and their derivatives. These methods are well studied and well established. However, both categories have some drawbacks related t...

Recently published studies have shown that partitional clustering algorithms that optimize certain criterion functions, which measure key aspects of interand intra-cluster similarity, are very effective in producing hard clustering solutions for document datasets and outperform traditional partitional and agglomerative algorithms. In this paper we study the extent to which these criterion funct...

Data clustering is considered as one of the most promising data analysis methods in data mining and on the other side KMeans is the well known partitional clustering technique. Nevertheless, K-Means and other partitional clustering techniques struggle with some challenges where dimension is the core concern. The different challenges associated with clustering techniques are preknowledge of init...