A Vibrating Method Based Cluster Reducing Strategy

Hsu, Daniel; Johnson, Shardrom

doi:10.1109/fskd.2008.425

Cited by 3 publications

(2 citation statements)

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“…Refer to the distillation of "energy", data object and clustering can be described by energy [3]. A strategy of clustering optimized by energy is taken to make the valid dynamic threshold among the cluster by energy.…”

Section: Introductionmentioning

confidence: 99%

Energy Description of Data Object and Clustering

Hsu

Johnson

Yang

2009

2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery

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Clustering is the hot spot in the study of Data Mining. In this paper, refer to the distillation of "energy", data object and clustering can be described by energy. With the function of clustering optimized by energy, energy driven about the user's demands will be brought into effect in all data-space, and all data object, including the outlier, is planed as a whole at the unified cognition platform. Through the experiment, a strategy of clustering optimized by energy is better than those which are just clustered based on density by using the CRCMD algorithm.

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Section: Introductionmentioning

confidence: 99%

Energy Description of Data Object and Clustering

Hsu

Johnson

Yang

2009

2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery

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“…The DBSCAN [30] is density fundamental cluster formation. Its advantage is that it can discover clusters with arbitrary shapes and sizes.…”

Section: Dbscan Algorithmmentioning

confidence: 99%

A Vibration Method for Discovering Density Varied Clusters

Elbatta

Bolbol

Ashour

2012

ISRN Artificial Intelligence

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DBSCAN is a base algorithm for density-based clustering. It can find out the clusters of different shapes and sizes from a large amount of data, which is containing noise and outliers. However, it is fail to handle the local density variation that exists within the cluster. Thus, a good clustering method should allow a significant density variation within the cluster because, if we go for homogeneous clustering, a large number of smaller unimportant clusters may be generated. In this paper, an enhancement of DBSCAN algorithm is proposed, which detects the clusters of different shapes and sizes that differ in local density. Our proposed method VMDBSCAN first finds out the "core" of each cluster-clusters generated after applying DBSCAN. Then, it "vibrates" points toward the cluster that has the maximum influence on these points. Therefore, our proposed method can find the correct number of clusters.

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