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Title of Journal: Pattern Anal Applic

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Abbravation: Pattern Analysis and Applications

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Springer-Verlag

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DOI

10.1016/0304-3991(94)90011-6

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1433-755X

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Clustering spatial data with a hybrid EM approach

Authors: Tianming Hu Sam Yuan Sung
Publish Date: 2005/07/19
Volume: 8, Issue: 1-2, Pages: 139-148
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Abstract

In spatial clustering in addition to the object similarity in the normal attribute space similarity in the spatial space needs to be considered and objects assigned to the same cluster should usually be close to one another in the spatial space The conventional expectation maximization EM algorithm is not suited for spatial clustering because it does not consider spatial information Although neighborhood EM NEM algorithm incorporates a spatial penalty term to the criterion function it involves much more iterations in every Estep In this paper we propose a Hybrid EM HEM approach that combines EM and NEM Its computational complexity for every pass is between EM and NEM Experiments also show that its clustering quality is better than EM and comparable to NEM

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