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An Efficient Clustering Algorithm for Large Databases Using Multiple Representative Points

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Lino Possamai

The document summarizes the CURE clustering algorithm, which uses a hierarchical approach that selects a constant number of representative points from each cluster to address limitations of centroid-based and all-points clustering methods. It employs random sampling and partitioning to speed up processing of large datasets. Experimental results show CURE detects non-spherical and variably-sized clusters better than compared methods, and it has faster execution times on large databases due to its sampling approach.

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Cure: An Efficient Clustering Algorithm for Large Databases Possamai Lino, 800509 Department of Computer Science University of Venice www.possamai.it/lino Data Mining Lecture - September 13th, 2006
Introduction ,[object Object],[object Object],[object Object],[object Object]
Drawbacks of Traditional Clustering Algorithms ,[object Object],[object Object],[object Object],[object Object]
Contribution of CURE, ideas ,[object Object],[object Object],[object Object],[object Object]
CURE architecture
Random Sampling ,[object Object],[object Object],[object Object],[object Object]
Partitioning sample ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Hierarchical Clustering Algorithm ,[object Object],[object Object],[object Object]
Hierarchical Clustering Algorithm ,[object Object],[object Object],[object Object],[object Object]
Example
Handling Outlier ,[object Object],[object Object],[object Object],[object Object],[object Object]
Labeling Data on Disk ,[object Object],[object Object],[object Object],[object Object]
Experimental Results ,[object Object],[object Object],[object Object],[object Object]
Experimental Results Quality of Clustering ,[object Object]
Experimental Results Sensitivity to Parameters ,[object Object]
Experimental Results Execution Time ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Conclusion ,[object Object],[object Object],[object Object]
Index ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object]

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An Efficient Clustering Algorithm for Large Databases Using Multiple Representative Points

  • 1. Cure: An Efficient Clustering Algorithm for Large Databases Possamai Lino, 800509 Department of Computer Science University of Venice www.possamai.it/lino Data Mining Lecture - September 13th, 2006
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