B. Eswara Reddy

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In unsupervised classification, kernel k-means clustering method has been shown to perform better than conventional k-means clustering method in identifying non-isotropic clusters in a data set. The space and time requirements of this method are O(n<sup>2</sup>), where n is the data set size. The paper proposes a two stage hybrid approach to speed-up the(More)
k-means clustering method is an iterative partition-based method which for finite data-sets converges to a solution in a finite time. The running time of this method grows linearly with respect to the size of the data-set. Many variants have been proposed to speed-up the conventional k-means clustering method. In this paper, we propose a prototype-based(More)
The objective of the present paper is to obtain an accurate classification of the textures, which did not introduce undesired merging and to develop a quick, effective and novel algorithm that should be easy to understand and implement. For this the present study advocates a new statistical method based on edge direction movement for classification of(More)
A suitable combination of linear and nonlinear models provides a more accurate prediction model than an individual linear or nonlinear model for forecasting time series data originating from various applications. The linear autoregressive integrated moving average (ARIMA) and nonlinear artificial neural network (ANN) models are explored in this paper to(More)
The Health Care System of a nation is the contribution of different areas like medical sciences, Biomedical engineering and Information Technology. Telemedicine is a promising technology which combines telecommunication and Information Technology for health care management. Telemedicine provides quality health care irrespective of socio economic and(More)
Kernel k-means clustering method has been proved to be effective in identifying non-isotropic and linearly inseparable clusters in the input space. However, this method is not a suitable one for large data-sets because of its quadratic time complexity with respect to the size of the data-set. This paper presents a simple prototype based hybrid approach to(More)
Feature subset selection is a process of selecting a subset of minimal, relevant features and is a pre processing technique for a wide variety of applications. High dimensional data clustering is a challenging task in data mining. Reduced set of features helps to make the patterns easier to understand. Reduced set of features are more significant if they(More)
Cloud data storage redefines the issues targeted on customer’s out-sourced data (data that is not stored/retrieved from the costumers own servers). In this work we observed that, from a customer’s point of view, relying upon a solo SP for his outsourced data is not very promising. In addition, providing better privacy as well as ensure data availability and(More)