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In this article, a distributed clustering technique, that is suitable for dealing with large data sets, is presented. This algorithm is actually a modified version of the very common k-means algorithm with suitable changes for making it executable in a distributed environment. For large input size, the running time complexity of k-means algorithm is very high and is measured as O(TKN), where K is...
Point symmetry-based clustering is an important unsupervised learning tool for recognizing symmetrical convex or non-convex shaped clusters, even in the microarray datasets. To enable fast clustering of this large data, in this article, a distributed space and time-efficient scalable parallel approach for point symmetry-based K-means algorithm has been proposed. A natural basis for analyzing gene...
An analysis of a parallel solution of N2-1 puzzle using clusters, is presented. This problem is interesting due to its complexity and related applications, particularly in the field of robotics. A variation of classic heuristics for forecasting the work to be done in order to reach a solution is analyzed, and it is shown that its use significantly improves the time of sequential algorithm A . Then,...
K-medians is a well-known clustering algorithm in data mining literature. This paper describes three decision problems related to k-medians. These problems are called medians replacement in k-medians, median determination, and T iterations k-medians problems. We show that these problems are in NC, each has a (logn)O(1) parallel time algorithm on an EREW PRAM model using nO(1) processors, and the result...
Supporting portable computers in a disconnected environment will require persistent caching of files without user intervention. SEER is a system that uses semantic information to predict which files the user is likely to work on, and arranges to transparently cache them on the portable platform prior to disconnection. We present the overall design of the SBBn system and the algorithms used to determine...
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