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- W1594334640 abstract "Statistical principles suggest minimization of the total within-group distance (TWGD) as a robust criterion for clustering point data associated with a Geographical Information System [17]. This NP-hard problem must essentially be solved using heuristic methods, although admitting a linear programming formulation. Heuristics proposed so far require quadratic time, which is prohibitively expensive for data mining applications. This paper introduces data structures for the management of large bi-dimensional point data sets and for fast clustering via interchange heuristics. These structures avoid the need for quadratic time through approximations to proximity information. Our scheme is illustrated with two-dimensional quadtrees, but can be extended to use other structures suited to three dimensional data or spatial data with time-stamps. As a result, we obtain a fast and robust clustering method.KeywordsExpectation MaximizationFacility LocationQuadratic TimeData Mining ApplicationOverlay NodeThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
- W1594334640 created "2016-06-24" @default.
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- W1594334640 date "2001-01-01" @default.
- W1594334640 modified "2023-09-23" @default.
- W1594334640 title "Data Structures for Minimization of Total Within-Group Distance for Spatio-temporal Clustering" @default.
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- W1594334640 doi "https://doi.org/10.1007/3-540-44794-6_8" @default.
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