Page 121 - Demo

%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%u0629121task is to find data groups with good clustering behavior that satisfy specified constraints. 8- Interpretability and usability: Users want clustering results to be interpretable, comprehensible, and usable. That is, clustering may need to be tied in with specific semantic interpretations and applications. It is important to study how an application goal may influence the selection of clustering features and clustering methods. Major Clustering Approaches: Partitioning methods: Construct various partitions and then evaluate them by some criterion, e.g., minimizing the sum of square errors .Typical methods are k-means, k-medoids, CLARANS. Hierarchical approach: Create a hierarchical decomposition of the set of data (or objects) using some criterion . Typical methods: Diana, Agnes, BIRCH, CAMELEON. Density-based approach: Based on connectivity and density functions . Typical methods: DBSACN, OPTICS, DenClue. Grid-based approach: based on a multiple-level granularity structure. Typical methods: STING, WaveCluster, CLIQUE . Model-based: A model is hypothesized for each of the clusters and tries to find the best fit of that model to each other. Typical methods: EM, SOM, COBWEB. Frequent pattern-based: Based on the analysis of frequent patterns . Typical methods: p-Cluster. User-guided or constraint-based: Clustering by considering user-specified or application-specific constraints . Typical methods: COD (obstacles), constrained clustering Link-based clustering: