DATA MINING Spatial Clustering

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Presentation transcript:

DATA MINING Spatial Clustering Margaret H. Dunham Department of Computer Science and Engineering Southern Methodist University Companion slides for the text by Dr. M.H.Dunham, Data Mining, Introductory and Advanced Topics, Prentice Hall, 2002. © Prentice Hall

Nearest Neighbor Items are iteratively merged into the existing clusters that are closest. Incremental Threshold, t, used to determine if items are added to existing clusters or a new cluster is created. © Prentice Hall

Nearest Neighbor Algorithm © Prentice Hall

PAM Partitioning Around Medoids (PAM) (K-Medoids) Handles outliers well. Ordering of input does not impact results. Does not scale well. Each cluster represented by one item, called the medoid. Initial set of k medoids randomly chosen. © Prentice Hall

PAM © Prentice Hall

PAM Cost Calculation At each step in algorithm, medoids are changed if the overall cost is improved. Cjih – cost change for an item tj associated with swapping medoid ti with non-medoid th. © Prentice Hall

PAM Algorithm © Prentice Hall

BIRCH Balanced Iterative Reducing and Clustering using Hierarchies Incremental, hierarchical, one scan Save clustering information in a tree Each entry in the tree contains information about one cluster New nodes inserted in closest entry in tree © Prentice Hall

Clustering Feature CT Triple: (N,LS,SS) N: Number of points in cluster LS: Sum of points in the cluster SS: Sum of squares of points in the cluster CF Tree Balanced search tree Node has CF triple for each child Leaf node represents cluster and has CF value for each subcluster in it. Subcluster has maximum diameter © Prentice Hall

BIRCH Algorithm © Prentice Hall

Improve Clusters © Prentice Hall

DBSCAN Density Based Spatial Clustering of Applications with Noise Outliers will not effect creation of cluster. Input MinPts – minimum number of points in cluster Eps – for each point in cluster there must be another point in it less than this distance away. © Prentice Hall

DBSCAN Density Concepts Eps-neighborhood: Points within Eps distance of a point. Core point: Eps-neighborhood dense enough (MinPts) Directly density-reachable: A point p is directly density-reachable from a point q if the distance is small (Eps) and q is a core point. Density-reachable: A point si density-reachable form another point if there is a path from one to the other consisting of only core points. © Prentice Hall

Density Concepts © Prentice Hall

DBSCAN Algorithm © Prentice Hall

CURE Clustering Using Representatives Use many points to represent a cluster instead of only one Points will be well scattered © Prentice Hall

CURE Approach © Prentice Hall

CURE Algorithm © Prentice Hall

CURE for Large Databases © Prentice Hall

Comparison of Clustering Techniques © Prentice Hall