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%u062c%u0645%u064a%u0639 %u0627%u0644%u062d%u0642%u0648%u0642 %u0645%u062d%u0641%u0648%u0638%u0629 %u0640 %u0627%u0625%u0644%u0639%u062a%u062f%u0627%u0621 %u0639%u0649%u0644 %u062d%u0642 %u0627%u0645%u0644%u0624%u0644%u0641 %u0628%u0627%u0644%u0646%u0633%u062e %u0623%u0648 %u0627%u0644%u0637%u0628%u0627%u0639%u0629 %u064a%u0639%u0631%u0636 %u0641%u0627%u0639%u0644%u0647 %u0644%u0644%u0645%u0633%u0627%u0626%u0644%u0629 %u0627%u0644%u0642%u0627%u0646%u0648%u0646%u064a%u0629116Chapter SevenBasic Concepts of Cluster AnalysisCluster analysis or simply clustering is the process of partitioning a set of data objects (or observations) into subsets. Each subset is a cluster, such that objects in a cluster are similar to one another, yet dissimilar to objects in other clusters. The set of clusters resulting from a cluster analysis can be referred to as a clustering. Cluster analysis is applied unsupervised learning I. Applications of cluster analysis Typical applications of cluster analysis :- As a stand-alone tool to get insight into data distribution and as a preprocessing step for other algorithms Applications of Cluster Analysis: 1- Data reduction : by summarization ( Preprocessing for regression, PCA, classification, and association analysis) and/or compression like :Image processing and vector quantization. 2- Hypothesis generation and testing 3- Prediction based on groups (cluster & find characteristics/patterns for each group) 4- Finding K-nearest Neighbors (Localizing search to one or a small number of clusters) 5- Outlier detection: Outliers are often viewed as those %u201cfar away%u201d from any cluster 6- Biology: taxonomy of living things: kingdom, phylum, class, order, family, genus and species 7- Information retrieval: document clustering 8- Land use: Identification of areas of similar land use in an earth observation database 9- Marketing: Help marketers discover distinct groups in their customer bases, and then use this knowledge to develop targeted marketing programs