Soft subspace clustering are effective clustering techniques for high dimensional datasets. Although several soft subspace clustering algorithms have been developed in recently years, its robustness should be further improved. In this work, a novel soft subspace clustering algorithm RSSKM are proposed. It is based on the incorporation of the alternative distance metric into the framework of kme...

Image segmentation is one of the analytical processes for digital image recognition, where this process divides into several unique regions based on homogeneous pixels. The grouping images colour, texture and shape features. Colour in processing very important because colour has many information humans can easily understand. various features, combining intensity grey (grayscale) binary (black w...

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...

We study kmeans clustering estimation of panel data models with a latent group structure and N units T time periods under long asymptotics. show that the group-specific coefficients can be estimated at parametric NT-rate even if error variances diverge as T→∞ consequently some are asymptotically misclassified. This limit case approximates empirically relevant settings is not covered by existing...

Data mining is the process of extraction of Hidden knowledge from the databases. Clustering is one the important functionality of the data mining Clustering is an adaptive methodology in which objects are grouped together, based on the principle of optimizing the inside class similarity and minimizing the class-class similarity. Various clustering algorithms have been developed resulting in a b...

Clustering is a useful technique that organizes a large quantity of unordered text documents into a small number of meaningful and coherent clusters, thereby providing a basis for intuitive and informative navigation and browsing mechanisms. Partitional clustering algorithms have been recognized to be more suitable as opposed to the hierarchical clustering schemes for processing large datasets....

In high dimensional data, general performance of the traditional clustering algorithms decreases. As some dimensions are likely to be irrelevant or contain noisy data and randomly selected initial centre of the clusters converge the clustering to local minima. In this paper, we propose a framework for clustering high dimensional data with attribute subset selection and efficient cluster centre ...

In this work we explore the possible enhancement of the document clustering results, and in particular clustering of news articles from the web, when using word-based n-grams during the keyword extraction phase. We present and evaluate a weighting approach that combines clustering of news articles derived from the web using n-grams, extracted from the articles at an offline stage. We compared t...

on the classification of high-dimensional data clustering analysis, traditional similarity index and dimension reduction based on clustering analysis method is hard to avoid "dimension disaster" problem or sampling errors. Therefore, on the basis of choosing the most sub space of the rough set theory, the article directly make a research of the classification of high dimensional data clustering...

We address the problem of communicating domain knowledge from a user to the designer of a clustering algorithm. We propose a protocol in which the user provides a clustering of a relatively small random sample of a data set. The algorithm designer then uses that sample to come up with a data representation under which kmeans clustering results in a clustering (of the full data set) that is alig...