NDLI: Uncertain data cluster based on DBSCAN

Content Provider	IEEE Xplore Digital Library
Author	Donghua Pan Lilei Zhao
Copyright Year	2011
Description	Author affiliation: Dalian University of Technology, Liaoning, 116024, China (Donghua Pan; Lilei Zhao)
Abstract	Uncertain data mining has recently attracted interests from researchers due to its presence in many applications such as Global Positioning System (GPS) Wireless Sensor Networks (WSN), Moving Object Tracking. This paper is researching uncertain data clustering problem, almost all the existed algorithms of uncertain data calculate expectation to express the distance of objects, so they can cluster like certain data. But they neglect the distribution of objects and consume much more running time to calculate expectation. In the paper, we propose CIR-DBSCAN, an algorithm based on a representation model of distance distribution between uncertain objects, which uses the Core Influence Rate (CIR) to extend the traditional DBSCAN algorithm in uncertain data. To evaluate its performance and accuracy, a comparison against the clustering algorithm FDBSCAN is performed using synthetic datasets. The experimental results show that the proposed algorithm CIR-DBSCAN outperforms FDBSCAN in some cases.
Starting Page	3781
Ending Page	3784
File Size	200063
Page Count	4
File Format	PDF
ISBN	9781612847719
e-ISBN	9781612847740
DOI	10.1109/ICMT.2011.6002707
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Iris Uncertainty Clustering algorithms Glass Probability density function DBSCAN Data mining Clustering Uncertain data
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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