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Spatial Interestingness Measures for Co-location Pattern Mining

Christian Sengstock, Michael Gertz, Tran Van Canh
2012 2012 IEEE 12th International Conference on Data Mining Workshops  
Co-location pattern mining aims at finding subsets of spatial features frequently located together in spatial proximity.  ...  In this paper, we present a new general class of interestingness measures that are based on the spatial distribution of co-location patterns.  ...  Contributions In this paper, we present a new class of interestingness measures describing the spatial characteristics of mined co-location patterns, which we call spatial interestingness measures of co-locations  ... 
doi:10.1109/icdmw.2012.116 dblp:conf/icdm/SengstockGC12 fatcat:nermn5qxevfvhanjxnlwlcearm

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

Christoph F. Eick, Rachana Parmar, Wei Ding, Tomasz F. Stepinski, Jean-Philippe Nicot
2008 Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems - GIS '08  
This paper proposes a novel framework for mining regional colocation patterns with respect to sets of continuous variables in spatial datasets.  ...  Interestingness of co-location patterns is assessed using products of z-scores of the relevant continuous variables.  ...  A MEASURE OF INTERESTINGNESS FOR REGIONAL CO-LOCATION PATTERNS In the following a function i is introduced that measures the interestingness of co-location patterns for a region c.  ... 
doi:10.1145/1463434.1463472 dblp:conf/gis/EickPDSN08 fatcat:fktkri2pwrfbbjv7yrixrxspp4

Regional co-location pattern scoping on a street network considering distance decay effects of spatial interaction

Wenhao Yu, Yanguang Chen
2017 PLoS ONE  
Regional co-location scoping intends to identify local regions where spatial features of interest are frequently located together.  ...  by calculating the co-location prevalence measure values, which are based on the density variation between different features.  ...  Region Planar co-location mining Network co-location mining Number of co-location instances Prevalence measure value Regional co-location pattern scoping in the real world.  ... 
doi:10.1371/journal.pone.0181959 pmid:28763496 pmcid:PMC5538746 fatcat:mf7p6lssljcoff47alfzbvms4i

A framework for spatial feature selection and scoping and its application to geo-targeting

Ruth Miller, ChunSheng Chen, Christoph F. Eick, Abraham Bagherjeiran
2011 Proceedings 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services  
In this paper, we present a novel framework for interestingness scoping to identify such regions and discuss how such interestingness hotspots can be used for geo-feature evaluation with the goal to develop  ...  more accurate prediction models for advertisers.  ...  Spatial Co-location Pattern Discovery Shekhar et al. discuss several interesting approaches to mine co-location patterns, which are subsets of Boolean spatial features whose instances are frequently located  ... 
doi:10.1109/icsdm.2011.5968999 dblp:conf/icsdm/MillerCEB11 fatcat:ndpp2ezedbdwnffpbkjpycl6ci

Fast Mining of Complex Spatial Co-location Patterns Using GLIMIT

Florian Verhein, Ghazi Al-Naymat
2007 Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007)  
Most algorithms for mining interesting spatial colocations integrate the co-location / clique generation task with the interesting pattern mining task, and are usually based on the Apriori algorithm.  ...  We use these to extract complex maximal cliques and subsequently mine these for interesting sets of object types (including complex types). That is, we mine interesting complex relationships.  ...  To the best of our knowledge, previous work has used Apriori type algorithms for mining interesting co-location patterns.  ... 
doi:10.1109/icdmw.2007.49 dblp:conf/icdm/VerheinA07 fatcat:rcrpzvmarrdr7gjcv2mie5ulm4

Correspondence Clustering: An Approach to Cluster Multiple Related Spatial Datasets [chapter]

Vadeerat Rinsurongkawong, Christoph F. Eick
2010 Lecture Notes in Computer Science  
Domain experts are frequently interested to analyze multiple related spatial datasets. This capability is important for change analysis and contrast mining.  ...  In addition, the paper proposes a novel cluster similarity assessment measure that relies on reclustering techniques and co-occurrence matrices.  ...  Regional co-location mining [2] that seeks for regions in which two types of events are co-located; for example, correspondence clustering can find regions where deep and severe earthquakes co-locate  ... 
doi:10.1007/978-3-642-13657-3_25 fatcat:gdrqug4qwjaztjtyom5rppunt4

On Regional Association Rule Scoping

Wei Ding, Christoph F. Eick, Xiaojing Yuan, Jing Wang, Jean-Philippe Nicot
2007 Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007)  
A special challenge for spatial data mining is that information is not distributed uniformly in spatial data sets. Consequently, the discovery of regional knowledge is of fundamental importance.  ...  Unfortunately, regional patterns frequently fail to be discovered due to insufficient global confidence and/or support in traditional association rule mining.  ...  Co-location mining [17] identifies a subset of boolean spatial features whose instances are frequently located together in close proximity.  ... 
doi:10.1109/icdmw.2007.26 dblp:conf/icdm/DingEYWN07 fatcat:mc6ov2ms4bcevc3vascl2gkwta

An Image Processing based Algorithm for Discovering Co-Location Patterns

Shahbaz Ahmad, Muhammad Asif
2016 International Journal of Computer Applications  
Spatial co-location patterns represents the subset of Boolean spatial features (e.g. Frontage roads, freeways) whose instances are often located in close geographic proximity.  ...  For instance, stagnant water founts and west Nile ailments are often co-located.  ...  As for future work, image processing based algorithms can be extended to mine line string co-location patterns.  ... 
doi:10.5120/ijca2016912338 fatcat:i37fysswtffbzmifuf6q4zv3ny

Cascading spatio-temporal pattern discovery: A summary of results [chapter]

Pradeep Mohan, Shashi Shekhar, James A. Shine, James P. Rogers
2010 Proceedings of the 2010 SIAM International Conference on Data Mining  
Given a collection of Boolean spatio-temporal(ST) event types, the cascading spatio-temporal pattern (CSTP) discovery process finds partially ordered subsets of event-types whose instances are located  ...  Existing literature for ST data mining focuses on mining totally ordered sequences or unordered subsets. In contrast, this paper models CSTPs as partially ordered subsets of Boolean ST event types.  ...  Acknowledgments We would like to thank Kim Koffolt and the members of the spatial database and data mining research group at the University of Minnesota for their helpful comments.  ... 
doi:10.1137/1.9781611972801.29 dblp:conf/sdm/MohanSSR10 fatcat:ms2p7hpz4rbwbmbi5h2j63ku7i

A Framework for Multi-Objective Clustering and Its Application to Co-Location Mining [chapter]

Rachsuda Jiamthapthaksin, Christoph F. Eick, Ricardo Vilalta
2009 Lecture Notes in Computer Science  
We evaluate the proposed MOC framework in a case study that centers on spatial co-location mining; the goal is to identify regions in which high levels of Arsenic concentrations are co-located with high  ...  The interestingness of an object o with respect to a co-location set B⊆Q is measured as the product of the z-values of the patterns in the set B.  ...  x) ) is used to assess interestingness for a region-which ignores alternative patterns: for example, if A is co-located with B,C in one region and with D,E in another region and the two regions overlap  ... 
doi:10.1007/978-3-642-03348-3_20 fatcat:clbtaztulffono2awvvxmavcde

A Study of Fusing Subjectivity and Distinct Issues in Data Mining

Sweta Kaushik, Dr. Rajiv Singh
2017 International Journal of Trend in Scientific Research and Development  
The measure of data appears to continue forever expanding and the advance in computerized data procurement and capacity innovation has brought about the development of immense databases.  ...  The proposed algorithm makes use of interestingness measure as the basis of extracting interesting patterns.  ...  In this work we introduce another measure of rule interestingness that is shocking rules and we propose an algorithm for incremental association rules mining that integrates shocking interestingness criterion  ... 
doi:10.31142/ijtsrd8228 fatcat:xm3tjmtcqjbe5nksivh73f45qy

GCG: Mining maximal complete graph patterns from large spatial data

Ghazi Al-Naymat
2013 2013 ACS International Conference on Computer Systems and Applications (AICCSA)  
In this paper, we describe GCG that can mine not only simple co-location spatial patterns but also complex ones.  ...  To the best of our knowledge, this is the first algorithm used to exploit the extraction of maximal complete graphs in the process of mining complex co-location patterns in large spatial dataset.  ...  So called frequent itemset mining uses the support as the measure of interestingness.  ... 
doi:10.1109/aiccsa.2013.6616417 dblp:conf/aiccsa/Al-Naymat13 fatcat:fu7yestsnve5zh5bio4wd35upu

Describing Locations Using Tags and Images: Explorative Pattern Mining in Social Media [chapter]

Florian Lemmerich, Martin Atzmueller
2012 Lecture Notes in Computer Science  
We utilize pattern mining techniques for obtaining sets of tags that are specific for the specified point, landmark, or region of interest.  ...  This paper presents an approach for explorative pattern mining in social media for describing image media based on tagging information and collaborative geo-reference annotations.  ...  Acknowledgment This work has partially been supported by the VENUS research cluster at the interdisciplinary Research Center for Information System Design (ITeG) at Kassel University, and by the EU project  ... 
doi:10.1007/978-3-642-33684-3_5 fatcat:vc2mmkl4vjbtfpkibdlrf4khza

On discovering co-location patterns in datasets: a case study of pollutants and child cancers

Jundong Li, Aibek Adilmagambetov, Mohomed Shazan Mohomed Jabbar, Osmar R. Zaïane, Alvaro Osornio-Vargas, Osnat Wine
2016 Geoinformatica  
Co-location mining is one of the tasks of spatial data mining which focuses on the detection of co-location patterns, the sets of spatial features frequently located in close proximity of each other.  ...  Co-location pattern analysis seems to be the appropriate investigation to perform.  ...  Conclusion Co-location pattern and rule mining is one of the tasks of spatial data mining.  ... 
doi:10.1007/s10707-016-0254-1 fatcat:kergmvkgtrgm7kebqsjtfo53je

CARM: Context Based Association Rule Mining for Conventional Data

Muhammad Shaheen, Umair Abdullah
2021 Computers Materials & Continua  
The context, in this paper serves as a pruning measure to extract pertinent association rules from non-spatial data.  ...  This paper is aimed to develop an algorithm for extracting association rules, called Context-Based Association Rule Mining algorithm (CARM), which can be regarded as an extension of the Context-Based Positive  ...  Funding Statement: The authors received no specific funding for this study. Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the present study.  ... 
doi:10.32604/cmc.2021.016766 fatcat:gdglg5apjraclakrts57vpgyqi
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