Constraint-Based Clustering. Why Constraint-Based Cluster Analysis? Need user feedback: Users know their applications the best Less parameters but more.

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Presentation transcript:

Constraint-Based Clustering

Why Constraint-Based Cluster Analysis? Need user feedback: Users know their applications the best Less parameters but more user-desired constraints, e.g., an ATM allocation problem: obstacle & desired clusters

A Classification of Constraints in Cluster Analysis Clustering in applications: desirable to have user-guided (i.e., constrained) cluster analysis Different constraints in cluster analysis: Constraints on individual objects (do selection first) Cluster on houses worth over $300K Constraints on distance or similarity functions Weighted functions, obstacles (e.g., rivers, lakes) Constraints on the selection of clustering parameters # of clusters, MinPts, etc. User-specified constraints Contain at least 500 valued customers and 5000 ordinary ones Semi-supervised: giving small training sets as “constraints” or hints

Clustering With Obstacle Objects K-medoids is more preferable since k-means may locate the ATM center in the middle of a lake Visibility graph and shortest path Triangulation and micro-clustering Two kinds of join indices (shortest- paths) worth pre-computation VV index: indices for any pair of obstacle vertices MV index: indices for any pair of micro-cluster and obstacle indices

An Example: Clustering With Obstacle Objects Taking obstacles into accountNot Taking obstacles into account

Clustering with User-Specified Constraints Example: Locating k delivery centers, each serving at least m valued customers and n ordinary ones Proposed approach Find an initial “solution” by partitioning the data set into k groups and satisfying user-constraints Iteratively refine the solution by micro-clustering relocation (e.g., moving δ μ-clusters from cluster C i to C j ) and “deadlock” handling (break the microclusters when necessary) Efficiency is improved by micro-clustering How to handle more complicated constraints? E.g., having approximately same number of valued customers in each cluster?! — Can you solve it?