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The amount of data kept in computer files and databases is growing at a phenomenal rate. At the same time the users of these data are expecting more sophisticated information from them. the problem of data mining or knowledge discovery has become increasingly important in recent years.there is an enormous wealth of information embedded in large data warehouses.Alternatively the data mining has been called exploratory data analysis,data driven discovery, and deductive learning. the clustering algorithm which is one of the data mining algorithms is useful technique for grouping data points such that points within a single group/cluster have similar characteristics.
This paper presents a Parallel Genetic Algorithm (PGA) based on the distributed (island) paradigm to optimize color image segmentation. The goal of using PGA is to accelerate the process of segmentation. However, that is not the only motivation for parallelism. Even when speed is not primary factor, these distributed algorithms, and as we shall see through the results, often outperform GAs with single population. Some examples in color images are presented and overall results discussed.
Color Image Segmentation --- Parallel Genetic Algorithms --- Clustering
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