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Title: A fast and efficient incremental Clustering algorithm for dynamic Data clustering
Researcher: Angel latha mary S
Guide(s): Shankar kumar K R
Keywords: Dynamic clustering algorithm
Normal Dynamic
Upload Date: 28-Apr-2015
University: Anna University
Completed Date: 01/02/2014
Abstract: The existing clustering algorithm integrates static components Most of the applications are converted into real time application It enforced that object to be clustered during the process based on its property There are many applications based on incremental data mining in data warehousing applications and sensor network Dynamic clustering is a mechanism to adopt the clustering in real time environments such as mobile computing war end movement observation etc Most of the supervised classification algorithms perform well and will give ideal results with good accuracy measured with normal accuracy metrics which is calculated using the original class labels and the calculated class labels However if the performance of cluster algorithm is measured with cluster validation metrics without any reference to class label then it will give entirely different result So even measuring the accuracy of a dynamic clustering algorithm is also a challenging task newlineIn this research a new density based dynamic data clustering algorithm is proposed and named as normal Dynamic DBSCAN newline
Pagination: xx, 147p.
Appears in Departments:Faculty of Information and Communication Engineering

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01_title.pdfAttached File25.87 kBAdobe PDFView/Open
02_certificate.pdf1.36 MBAdobe PDFView/Open
03_abstract.pdf263.59 kBAdobe PDFView/Open
04_acknowledgement.pdf55.42 kBAdobe PDFView/Open
05_content.pdf705.72 kBAdobe PDFView/Open
06_chapter1.pdf376.72 kBAdobe PDFView/Open
07_chapter2.pdf2.06 MBAdobe PDFView/Open
08_chapter3.pdf2.08 MBAdobe PDFView/Open
09_chapter4.pdf1.14 MBAdobe PDFView/Open
10_chapter5.pdf97.25 kBAdobe PDFView/Open
11_appendix.pdf1.88 MBAdobe PDFView/Open
12_reference.pdf339.55 kBAdobe PDFView/Open
13_publication.pdf56.34 kBAdobe PDFView/Open

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