Text clustering is one of the difficult and hot research fields in the internet search engine research. A new text clustering algorithm is presented based on Kmeans and Self-Organizing Model (SOM). Firstly, texts are preprocessed to satisfy succeed process requirement. Secondly, the paper improves selection of initial cluster centers and cluster seed selection methods of K-means to improve the ...

Data mining is the mechanism of implementing patterns in large amount of data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. Clustering is the very big area in which grouping of same type of objects in data mining. Clustering has divided into different categories – partitioned clustering and hierarchical clustering. In ...

Instant intercommunion techniques such as Instant Messaging (IM) are widely popularized. Aiming at such kind of large scale masscommunication media, clustering on its text content is a practical method to analyze the characteristic of text content in instant messages, and find or track the social hot topics. However, key words in one instant message usually are few, even latent; moreover, sing...

In this paper we present a comparison of multiple cluster algorithms and their suitability for clustering text data. The clustering is based on similarities only, employing the Kolmogorov complexity as a similiarity measure. This motivates the set of considered clustering algorithms which take into account the similarity between objects exclusively. Compared cluster algorithms are Median kMeans...

Due to the recent emergence Clustering techniques have been widely adopted in many real world data analysis applications, such as customer behavior analysis, targeted marketing, digital forensics, etc. As the satellite imagery is getting generated at a higher rate than the previous decades, it becomes essential to have better solutions in terms of accuracy as well as performance. In this paper,...

Clustering is the process of grouping of data, where the grouping is established by finding similarities between data based on their characteristics. Such groups are termed as Clusters. A comparative study of clustering algorithms across two different data items is performed here. The performance of the various clustering algorithms is compared based on the time taken to form the estimated clus...

Aiming at the problems of traditional K-means clustering algorithm, such as local optimal solution and slow speed caused by uncertainty k value randomness initial cluster center selection, this paper proposes an improved KMeans method. The algorithm first uses idea elbow rule based on sum squares errors to obtain appropriate number clusters k, then variance a measure degree dispersion samples, ...

Document clustering is a powerful technique that has been widely used for organizing data into smaller and manageable information kernels. Several approaches have been proposed suffering however from problems like synonymy, ambiguity and lack of a descriptive content marking of the generated clusters. We are proposing the enhancement of standard kmeans algorithm using the external knowledge fro...

Noise removal is a vital role in medical imaging, such as in magnetic resonance imaging (MRI). So in order to preserve the important features and to guarantee the correct diagnosis, the authors have proposed a new method for removing noise based on NL-mean filter and diffusion tensor. This paper presents a comparison of the MRI slices images segmentation extracted from a some 3D denoised techni...

This paper focuses on the enhanced initial centroids for the K-means algorithm. The original kmeans is using the random choice of init ial seeds which is a major limitation of the orig inal K-means algorithm because it produces less reliab le result of clustering the data. The enhanced method of the k-means algorithm includes the computation of the weighted mean to improve the centroids initial...