Towards Region Discovery in Spatial Datasets

Ding, Wei; Jiamthapthaksin, Rachsuda; Parmar, Rachana; Jiang, Dan; Stepinski, T. F.; Eick, Christoph F.

doi:10.1007/978-3-540-68125-0_10

Cited by 16 publications

(7 citation statements)

References 12 publications

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“…The region discovery framework [1] gears towards finding scientifically interesting places in spatial datasets. The framework adapts clustering algorithms for the task of region discovery by allowing plug-in fitness functions to support variety of region discovery applications corresponding to different domain interests.…”

Section: Region Discovery Frameworkmentioning

confidence: 99%

Correspondence Clustering: An Approach to Cluster Multiple Related Spatial Datasets

Rinsurongkawong

Eick

2010

Advances in Knowledge Discovery and Data Mining

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Abstract. Domain experts are frequently interested to analyze multiple related spatial datasets. This capability is important for change analysis and contrast mining. In this paper, a novel clustering approach called correspondence clustering is introduced that clusters two or more spatial datasets by maximizing cluster interestingness and correspondence between clusters derived from different datasets. A representative-based correspondence clustering framework and clustering algorithms are introduced. In addition, the paper proposes a novel cluster similarity assessment measure that relies on reclustering techniques and co-occurrence matrices. We conducted experiments in which two earthquake datasets had to be clustered by maximizing cluster interestingness and agreement between the spatial clusters obtained. The results show that correspondence clustering can reduce the variance inherent to representative-based clustering algorithms, which is important for reducing the likelihood of false positives in change analysis. Moreover, high agreements could be obtained by only slightly lowering cluster quality.

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Section: Region Discovery Frameworkmentioning

confidence: 99%

Correspondence Clustering: An Approach to Cluster Multiple Related Spatial Datasets

Rinsurongkawong

Eick

2010

Advances in Knowledge Discovery and Data Mining

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“…The presented framework has originally been introduced in [10,11], and will be generalized in this section to mine spatio-temporal datasets that contain multiple continuous variables. A novel measure of interestingness for mining co-locations involving continuous attributes that is embedded into this framework will be introduced in Section 3.…”

Section: Region Discovery Frameworkmentioning

confidence: 99%

Finding regional co-location patterns for sets of continuous variables in spatial datasets

Eick

Parmar

Ding

et al. 2008

Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

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This paper proposes a novel framework for mining regional colocation patterns with respect to sets of continuous variables in spatial datasets. The goal is to identify regions in which multiple continuous variables with values from the wings of their statistical distribution are co-located. A co-location mining framework is introduced that operates in the continuous domain without the need for discretization and which views regional co-location mining as a clustering problem in which an externally given fitness function has to be maximized. Interestingness of co-location patterns is assessed using products of z-scores of the relevant continuous variables. The proposed framework is evaluated by a domain expert in a case study that analyzes Arsenic contamination in Texas water wells centering on regional co-location patterns. Our approach is able to identify known and unknown regional colocation patterns, and different sets of algorithm parameters lead to the characterization of Arsenic distribution at different scales. Moreover, inconsistent co-location sets are found for regions in South Texas and West Texas that can be clearly attributed to geological differences in the two regions, emphasizing the need for regional co-location mining techniques. Moreover, a novel, prototype-based region discovery algorithm named CLEVER is introduced that uses randomized hill climbing, and searches a variable number of clusters and larger neighborhood sizes.

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“…The dependency is based on Tobler's First Law of Geography which states "Everything is related to everything else, but near things are more related than distant things" (Tobler 1970). As one of important spatial data mining tasks, spatial colocation pattern mining has been popularly studied for discovering the spatial dependency of objects (Shekhar and Huang 2001;Shekhar 2004, 2006;Eick et al 2008;Morimoto 2001;Koperski and Han 1995;Ding et al 2008;Xiao et al 2008). A spatial colocation pattern represents "a set of spatial features which are frequently observed together in a spatial proximity" (Shekhar and Huang 2001).…”

Section: Introductionmentioning

confidence: 99%

Mining spatial colocation patterns: a different framework

Yoo

Bow

2011

Data Min Knowl Disc

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Recently, there has been considerable interest in mining spatial colocation patterns from large spatial datasets. Spatial colocation patterns represent the subsets of spatial events whose instances are often located in close geographic proximity. Most studies of spatial colocation mining require the specification of two parameter constraints to find interesting colocation patterns. One is a minimum prevalent threshold of colocations, and the other is a distance threshold to define spatial neighborhood. However, it is difficult for users to decide appropriate threshold values without prior knowledge of their task-specific spatial data. In this paper, we propose a different framework for spatial colocation pattern mining. To remove the first constraint, we propose the problem of finding N -most prevalent colocated event sets, where N is the desired number of colocated event sets with the highest interest measure values per each pattern size. We developed two alternative algorithms for mining the N -most patterns. They reduce candidate events effectively and use a filter-and-refine strategy for efficiently finding colocation instances from a spatial dataset. We prove the algorithms are correct and complete in finding the N -most prevalent colocation patterns. For the second constraint, a distance threshold for spatial neighborhood determination, we present various methods to estimate appropriate distance bounds from user input data. The result can help an user to set a distance for a conceptualization of spatial neighborhood. Our experimental results with real and synthetic datasets show that our algorithmic design is computationally effective in finding the N -most prevalent colocation patterns. The discovered patterns were different depending on the distance threshold, which shows that it is important to select appropriate neighbor distances.Responsible editor: M.J. Zaki.

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Towards Region Discovery in Spatial Datasets

Cited by 16 publications

References 12 publications

Correspondence Clustering: An Approach to Cluster Multiple Related Spatial Datasets

Correspondence Clustering: An Approach to Cluster Multiple Related Spatial Datasets

Finding regional co-location patterns for sets of continuous variables in spatial datasets

Mining spatial colocation patterns: a different framework

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