NDLI: A Generative Spatial Clustering Model for Random Data through Spanning Trees

Content Provider	IEEE Xplore Digital Library
Author	Vilela Teixeira, L. Martins Assuncao, R. Loschi, R.H.
Copyright Year	2015
Description	Author affiliation: Univ. Fed. de Minas Gerais, Belo Horizonte, Brazil (Vilela Teixeira, L.; Martins Assuncao, R.; Loschi, R.H.)
Abstract	When performing analysis of spatial data, there is often the need to aggregate geographical areas into larger regions, a process called regionalization or spatially constrained clustering. These algorithms assume that the items to be clustered are non-stochastic, an assumption not held in many applications. In this work, we present a new probabilistic regionalization algorithm that allows spatially varying random variables as features. Hence, an area highly different from its neighbors can still be considered a member of their cluster if it has a large variance. Our proposal is based on a Bayesian generative spatial product partition model. We build an effective Markov Chain Monte Carlo algorithm to carry out a random walk on the space of all trees and their induced spatial partitions by edges' deletion. We evaluate our algorithm using synthetic data and with one problem of municipalities regionalization based on cancer incidence rates. We are able to better accommodate the natural variation of the data and to diminish the effect of outliers, producing better results than state-of-art approaches.
Starting Page	997
Ending Page	1002
File Size	10684725
Page Count	6
File Format	PDF
ISSN	15504786
e-ISBN	9781467395045
DOI	10.1109/ICDM.2015.106
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Yttrium Partitioning algorithms Clustering algorithms Data models Stochastic processes Probability distribution Space exploration spatial graphical model spatially constrained clustering Bayesian spatial model
Content Type	Text
Resource Type	Article

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