NDLI: 4D+SNN: A Spatio-Temporal Density-Based Clustering Approach with 4D Similarity

Content Provider	IEEE Xplore Digital Library
Author	Oliveira, R. Santos, M.Y. Moura Pires, J.
Copyright Year	2013
Description	Author affiliation: ALGORITMI Res. Centre, Univ. of Minho, Guimaraes, Portugal (Oliveira, R.; Santos, M.Y.) \|\| Fac. of Sci. & Technol., New Univ. of Lisbon, Lisbon, Portugal (Moura Pires, J.)
Abstract	Spatio-temporal clustering is a sub field of data mining that is increasingly gaining more scientific attention due to the advances of location-based or environmental devices that register position, time and, in some cases, other semantic attributes. This process pretends to group objects based in their spatial and temporal similarity helping to discover interesting patterns and correlations in large data sets. One of the main challenges of this area is the ability to integrate several dimensions in a general-purpose approach. In this paper, such general approach is proposed, based on an extension of the SNN (Shared Nearest Neighbor) algorithm. The 4D+SNN algorithm allows the integration of space, time and one or more semantic attributes in the clustering process. This algorithm is able to deal with different data sets and different discovery purposes as the user has the ability to weight the importance of each dimension in the discovery process. The results obtained are very promising as show interesting findings on data and open the possibility of integration of several dimensions of analysis in the clustering process.
Sponsorship	Toshiba
Starting Page	1045
Ending Page	1052
File Size	1256673
Page Count	8
File Format	PDF
e-ISBN	9781479931422
DOI	10.1109/ICDMW.2013.119
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 spatio-temporal clustering distance function density-based clustering Semantics Noise Clustering algorithms Fires clustering Data mining Object recognition spatiotemporal data
Content Type	Text
Resource Type	Article

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