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Determining the number of clusters present in a data set automatically is a very important problem. Conventional clustering techniques assume a certain number of clusters, and then try to find out the possible cluster structure associated to the above number. For very large and complex data sets it is not easy to guess this number of clusters. There exists validity based clustering techniques, which...
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...
Visual optimization is a very interesting topic to the application users for many purposes. It enables the user with an interactive platform where, by varying different parameter settings, one can customize a solution. Several attempts of developing generalized evolutionary optimizers are found in literature which work well for function optimization problems only. Solving combinatorial optimization...
The CLARA algorithm is one of the popular clustering algorithms in use nowadays. This algorithm works on a randomly selected subset of the original data and produces near accurate results at a faster rate than other clustering algorithms. CLARA is basically used in data mining applications. We have used this algorithm for color image segmentation.The original CLARA is modified for producing better...
Visual optimization is a very interesting topic to the application users for many purposes. It enables the user with an interactive platform where, by varying different parameter settings, one can customize a solution. Several attempts of developing generalized evolutionary optimizers are found in literature. In this paper, we have tried to develop a generalized visual platform for stochastic optimization...
A novel sample based clustering technique has been developed in this paper. Since traditional k-means algorithm is very time consuming for large disk resident data, sample based, out-of-core clustering techniques have gained high popularity recently. We have used the concept of elimination of measurement errors by averaging over a number of samples. Here, samples or original data set are chosen randomly,...
Genetic algorithms are a class of stochastic optimization techniques inspired by biological evolution processes. The power of GAs for solving complex problems is highly used in the design of parallel problem solving machines. High parallelism needs higher number of parallel processors to be used simultaneously. This approach may be costly in terms of efficiency and utilization of processors. GAs are...
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