NDLI: Dynamic Analytics for Spatial Data with an Incremental Clustering Approach

Content Provider	IEEE Xplore Digital Library
Author	Mendes, F. Santos, M.Y. Moura-Pires, J.
Copyright Year	2013
Description	Author affiliation: ALGORITMI Res. Centre, Univ. of Minho, Guimaraes, Portugal (Mendes, F.; Santos, M.Y.) \|\| Fac. of Sci. & Technol., New Univ. of Lisbon, Lisbon, Portugal (Moura-Pires, J.)
Abstract	Several clustering algorithms have been extensively used to analyze vast amounts of spatial data. One of these algorithms is the SNN (Shared Nearest Neighbor), a density-based algorithm, which has several advantages when analyzing this type of data due to its ability of identifying clusters of different shapes, sizes and densities, as well as the capability to deal with noise. Having into account that data are usually progressively collected as time passes, incremental clustering approaches are required when there is the need to update the clustering results as new data become available. This paper proposes $SNN^{++},$ an incremental clustering algorithm based on the SNN. Its performance and the quality of the resulting clusters are compared with the SNN and the results show that the $SNN^{++}$ yields the same result as the SNN and show that the incremental feature was added to the SNN without any computational penalty. Moreover, the experimental results also show that processing huge amounts of data using increments considerably decreases the number of distances that need to be computed to identify the points' nearest neighbors.
Sponsorship	Toshiba
Starting Page	552
Ending Page	559
File Size	1784745
Page Count	8
File Format	PDF
e-ISBN	9781479931422
DOI	10.1109/ICDMW.2013.169
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis spatial data Shape incremental clustering Noise shared nearest neighbor Clustering algorithms clustering Classification algorithms Partitioning algorithms Object recognition
Content Type	Text
Resource Type	Article

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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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