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...

with rapid development in information gathering technologies and access to large amounts of data, we always require methods for data analyzing and extracting useful information from large raw dataset and data mining is an important method for solving this problem. clustering analysis as the most commonly used function of data mining, has attracted many researchers in computer science. because o...

Text Document Clustering is one of the fastest growing research areas because of availability of huge amount of information in an electronic form. There are several number of techniques launched for clustering documents in such a way that documents within a cluster have high intra-similarity and low inter-similarity to other clusters. Many document clustering algorithms provide localized search...

The clustering algorithms have evolved over the last decade. With the continuous success of natural inspired algorithms in solving many engineering problems, it is imperative to scrutinize the success of these methods applied to data clustering. These naturally inspired algorithms are mainly stochastic search and optimization techniques, guided by the principles of collective behavior and self-...

With rapid development in information gathering technologies and access to large amounts of data, we always require methods for data analyzing and extracting useful information from large raw dataset and data mining is an important method for solving this problem. Clustering analysis as the most commonly used function of data mining, has attracted many researchers in computer science. Because o...

Clustering is a widely studied problem in data mining. Ai techniques, evolutionary techniques and optimization techniques are applied to this field. In this study, a novel hybrid modeling approach proposed for clustering and feature selection. Ant colony clustering technique is used to segment breast cancer data set. To remove irrelevant or redundant features from data set for clustering Sequen...

Bijayalaxmi Panda, Soumya Sahoo, Sovan Kumar Patnaik Abstract— Cluster analysis is one of the major techniques in pattern recognition, which is basically considered as one of the unsupervised learning technique. We can apply clustering techniques in various areas like clustering medicine, business, engineering systems and image processing, etc.,The traditional hard clustering methods restrict t...

Image segmentation plays an essential role in the interpretation of various kinds of images. Image segmentation techniques can be grouped into several categories such as edge-based segmentation, region-oriented segmentation, histogram thresholding, and clustering algorithms (Gonzalez & Woods, 1992). The aim of a clustering algorithm is to aggregate data into groups such that the data in each gr...

Since minimum sum-of-squares clustering (MSSC) is an NP hard combinatorial optimization problem, applying techniques from global optimization appears to be promising for reliably clustering numerical data. In this paper, concepts of combinatorial heuristic optimization are considered for approaching the MSSC: An iterated local search (ILS) approach is proposed which is capable of finding (near-...