NDLI: DBSCALE: An efficient density-based clustering algorithm for data mining in large databases

Content Provider	IEEE Xplore Digital Library
Author	Cheng-Fa Tsai Chun-Yi Sung
Copyright Year	2010
Description	Author affiliation: Department of Management Information Systems, National Pingtung University of Science and Technology, 91201, Taiwan (Cheng-Fa Tsai; Chun-Yi Sung)
Abstract	This work presents a novel clustering algorithm that incorporates neighbor searching and expansion seed selection into a density-based clustering algorithm. Data Points that have been clustered need not be input again when searching for neighborhood data points, and the algorithm redefines eight Marked Boundary Objects to add expansion seeds according to far centrifugal force, which increases coverage. Experimental results indicate that the proposed DBSCALE has a lower execution time cost than DBSCAN, mBSCAN and KIDBSCAN clustering algorithms. DBSCALE has a maximum deviation in clustering correctness rate of 0.29%, and a maximum deviation in noise data clustering rate of 0.14%.
Starting Page	98
Ending Page	101
File Size	585601
Page Count	4
File Format	PDF
ISBN	9781424479696
e-ISBN	9781424479702
DOI	10.1109/PACCS.2010.5627040
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-01
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	density-based clustering algorithm Databases Clustering algorithms data mining data clustering Partitioning algorithms
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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DBSCALE: An efficient density-based clustering algorithm for data mining in large databases