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DATA MINING Spatial Clustering

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Presentation on theme: "DATA MINING Spatial Clustering"— Presentation transcript:

1 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

2 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

3 Nearest Neighbor Algorithm
© Prentice Hall

4 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

5 PAM © Prentice Hall

6 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

7 PAM Algorithm © Prentice Hall

8 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

9 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

10 BIRCH Algorithm © Prentice Hall

11 Improve Clusters © Prentice Hall

12 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

13 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

14 Density Concepts © Prentice Hall

15 DBSCAN Algorithm © Prentice Hall

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

17 CURE Approach © Prentice Hall

18 CURE Algorithm © Prentice Hall

19 CURE for Large Databases
© Prentice Hall

20 Comparison of Clustering Techniques
© Prentice Hall

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