Major Clustering Approaches o Partitioning approach Construct various partitions and then evaluate them by some criterion, e.g., minimizing the sum of square errors Typical methods: 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 10Major Clustering Approaches (I) ◼ Partitioning approach: ◼ Construct various partitions and then evaluate them by some criterion, e.g., minimizing the sum of square errors ◼ Typical methods: 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 10