This paper presents a personalized approach for distributed trust management by employing the k-means range algorithm, a combination of the partitional kmeans clustering algorithm with orthogonal range search concepts. The aim of this approach is to aid the human or computer agent in organizing information from multiple sources into clusters according to its “trust features”. Thus the agent can...

Social image datasets have grown to dramatic size with images classified in vector spaces with high dimension (512-2048) and with potentially billions of images and corresponding classification vectors. We study the challenging problem of clustering such sets into millions of clusters using Iterative MapReduce. We introduce a new Kmeans algorithm in the Map phase which can tackle the challenge ...

Data mining algorithms extracts the unknown interesting patterns from large collection of data set. Some clandestine or secret information may be exposed as part of the data mining process. In this paper we put forward a hybrid approach for achieving privacy during the mining procedure. The first step is to sanitize the original data using a geometrical data transformation. In the second stage ...

Application of document clustering techniques to cluster e-mails is an interesting application. Techniques like kmeans, EM etc can be used to achieve this. However, the selection of a good distance metric is the key issue involved. Often people manually tweak the chosen distance metric to achieve desirable/good clusters/results that in all certainty do not provide a generic solution. Hence it w...

While Bregman divergences [3] have been used for several machine learning problems in recent years, the facts that they are asymmetric and does not satisfy triangle inequality have been a major limitation. In this paper, we investigate the relationship between two families of symmetrized Bregman divergences and metrics, which satisfy the triangle inequality. Further, we investigate kmeans-type ...

Clustering method is divided into hierarchical clustering, partitioning clustering, and more. K-Means algorithm is one of partitioning clustering methods and is adequate to cluster a lot of data rapidly and easily. The problem is it is too dependent on initial centers of clusters and needs the time of allocation and recalculation. We compare random method, max average distance method and triang...

Customer clustering is used to understand customers’ preferences and behaviors by examining the differences and similarities between customers. Kohonen vector quantization clustering technology is used in this research and is compared with Kmeans clustering. The data set consists of customer records obtained from a mobile telecommunications service provider. The customers are clustered using va...

We propose an acceleration that computes the same answer as the standard kmeans algorithm in substantially less time. Like Hamerly’s [3] and Elkan’s [2] accelerated algorithms, we avoid unnecessary distance calculations using distance bounds. For each point, Hamerly keeps one lower bound and Elkan keeps k, but we keep b lower bounds, where 1 < b < k. Our algorithm adaptively selects and adjusts...

Image classification is a well known of the significant tools used to recognize and examine most sharp information in satellite images. In any remote sensing research, the decision-making way mainly rely on the efficiency of the classification process. There are disparate classification algorithms on the large satellite imagery: Multilayer perceptron back propagation neural network (MLP BPNN), ...

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...