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Minimum Spanning Tree Partitioning Algorithm fo

r Microaggregation

報告者：林惠珍

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Outline

The Microaggregation problem MST partitioning algorithm Experimental results Conclusions and future work

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The Microaggregation problem

Microdata set( n records, p numerical attributes)

To partition the n points into groups so as to minimize the objective function： SSE

subject to

It is equivalent to minimizing a standardized information loss measure L= SSE / SST.

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Microaggregation vs. clustering problem

Constraint

Problem

The number of clusters

cluster size

Microaggregation No Yes

Clustering Yes No

So, modify the strategy for selecting edges for deletion from the MST for microaggregation problem. In decreasing length order Each tree has at least k nodes

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MST Partitioning Algorithm

MST construction( Prim’s algorithm) Edge cutting Cluster formation

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k=5

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Experimental methods

D: Diameter-based fixed size method C: Centroid-based fixed size method M: MST-partitioning algorithm M-d: MST-partitioning followed by clusters of

size >= 2k partitioned by D M-c: MST-partitioning followed by clusters of

size >= 2k partitioned by C

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Experimental results

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Conclusions

The more pronounced the inherent clustering effects in the data, the greater is the advantage of using the our methods.

MST partitioning-based method should be considered as a potential candidate for any practical application.

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Future work

To adapt some of the ideas used to solve other clustering problems to this constrained version.

To explore methods where minimum group size is treated as a soft constraint associated with a preference level.