Clustering is a crucial step in the analysis of gene expression data. Its goal is to identify the natural clusters and provide a reliable estimate of the number of distinct clusters in a given data set. In this paper we propose new hybrid algorithm for clustering of microarray data based on spectral clustering and k-means. Our algorithm consist of four steps, including preprocessing or filterin...

Tibetan text clustering has potential in Tibetan information processing domain. In this paper, clustering research across Chinese and Tibetan texts is proposed to benefit Chinese and Tibetan machine translation and sentence alignment. A Tibetan and Chinese keyword table is the main way to implement the text clustering across these two languages. Improved Kmeans and improved density-based spatia...

This article explores data mining techniques in health care. In particular, it discusses data mining and its application in areas where people are affected severely by using the underground drinking water which consist of high levels of fluoride in Krishnagiri District, Tamil Nadu State, India. This paper identifies the risk factors associated with the high level of fluoride content in water, u...

We investigate the role of the initialization for the stability of the kmeans clustering algorithm. As opposed to other papers, we consider the actual k-means algorithm and do not ignore its property of getting stuck in local optima. We are interested in the actual clustering, not only in the costs of the solution. We analyze when different initializations lead to the same local optimum, and wh...

Logika fuzzy adalah salah satu komponen yang membentuk komputasi lunak, merupakan cara mudah untuk memetakan ruang input ke output. Dalam banyak kasus, logika digunakan menyelesaikan masalah dari hingga sering hal ini Fuzzy C-Means Clustering akan dalam jurnal ini. CMeans (FCM) atau dikenal dengan ISODATA bagian metode KMeans. Derajat keberadaan data suatu kelas kelompok ditentukan oleh derajat...

In this study a clustering technique has been implemented which is K-Means like with hierarchical initial set (HKM). The goal of this study is to prove that clustering document sets do enhancement precision on information retrieval systems, since it was proved by Bellot & El-Beze on French language. A comparison is made between the traditional information retrieval system and the clustered one....

Clustering technique is one of the most important research areas in the field of data mining. This paper proposes an improved K-Means clustering algorithm form partition based clustering algorithms. It determines the initial centroid of the cluster and gives more efficient performance and reduces the time complexity of the large data sets. The data set used here is banking data. Fuzzy C-Means c...

Clustering is one of the widely using data mining technique that is used to place data elements into allied groups of “similar behavior”. The conventional clustering algorithm called K-Means algorithm has some well-known problems, i.e., it does not work properly on clusters with not well defined centers, it is difficult to choose the number of clusters to construct different initial centers can...

The present work proposes hybridization of Expectation-Maximization (EM) and KMeans techniques as an attempt to speed-up the clustering process. Though both K-Means and EM techniques look into different areas, K-means can be viewed as an approximate way to obtain maximum likelihood estimates for the means. Along with the proposed algorithm for hybridization, the present work also experiments wi...