By taking advantage of both k-NN which is highly accurate and K-means cluster which is able to reduce the time of classification, we can introduce Cluster-k-Nearest Neighbor as ”variable k”-NN dealing with the centroid or mean point of all subclasses generated by clustering algorithm. In general the algorithm of K-means cluster is not stable, in term of accuracy, for that reason we develop anot...

Rough K-means algorithm has shown that it can provides a reasonable set of lower and upper bounds for a given dataset. With the conceptions of the lower and upper approximate sets, rough k-means clustering and its emerging derivatives become valid algorithms in vague information clustering. However, the most available algorithms ignore the difference of the distances between data objects and cl...

In this study, the general ideas surrounding the k-medians problem are discussed. This involves a look into what k-medians attempts to solve and how it goes about doing so. We take a look at why k-medians is used as opposed to its k-means counterpart, specifically how its robustness enables it to be far more resistant to outliers. We then discuss the areas of study that are prevalent in the rea...

Document clustering is useful in many information retrieval tasks such as document browsing, organization and viewing of retrieval results. They are very much and currently the subject of significant global research. Generative models based on the multivariate Bernoulli and multinomial distributions have been widely used for text classification. In this work, address a new hybrid algorithm call...

Data Mining is the process of extract useful information from the large data by using different mining techniques. Clustering and classification manage the large amount of data into different clusters according to their properties. Sometimes data arranged in clusters contain outliers that degrade the performance of the system. The outliers detected by K-Mean genetic algorithm also contain infor...

We propose a novel Mean-Shift method for data clustering, called Robust (RMS). A new update equation point iterates is proposed, mixing the ones of standard (MS) and Blurring (BMS). Despite its simplicity, proposed has not been studied so far. RMS can be set up in both kernel-based nearest-neighbor (NN)-based fashion. Since rule closer to BMS, convergence conjectured based on Chen’s BMS theorem...

Clustering techniques are used to group up the transactions based on the relevancy. Cluster analysis is one of the primary data analysis method. The clustering process can be done in two ways such that Hierarchical clusters and partition clustering. Hierarchical clustering technique uses the structure and data values. The partition clustering technique uses the data similarity factors. Transact...

In 2009, Yu et al. proposed a multimod al probability model (MPM) for clustering. This paper makes advanced clustering constructions on the MPM. We first reconstruct most existing clustering algorithms, such as the k-means, fuzzy c-means, possibilistic c-means, mean shift, classification maximum likelihood, and latent class methods, by establishing the relationships between these clustering alg...