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A framework for regional association rule mining and scoping in spatial datasets

Wei Ding, Christoph F. Eick, Xiaojing Yuan, Jing Wang, Jean-Philippe Nicot
2010 Geoinformatica  
The motivation for regional association rule mining and scoping is driven by the facts that global statistics seldom provide useful insight and that most relationships in spatial datasets are geographically  ...  In particular, we present a reward-based region discovery framework that employs a divisive grid-based supervised clustering for region discovery.  ...  The Framework for Regional Association Rule Mining and Scoping The framework of regional association rule mining and scoping consists of three steps: Step 1 Region Discovery: identifying interesting regions  ... 
doi:10.1007/s10707-010-0111-6 fatcat:uqsbt5o6ijca3pbrwah7gasmhq

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)  
This paper centers on regional association rule scoping. We introduce a reward-based region discovery framework that employs clustering to find places where regional association rules are valid.  ...  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.  ...  Spatial association rule mining [10] extends association rule mining to spatial data sets.  ... 
doi:10.1109/icdmw.2007.26 dblp:conf/icdm/DingEYWN07 fatcat:mc6ov2ms4bcevc3vascl2gkwta

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  ...  Identifying regions with strong associations of a continuous performance attribute with geo-features can create valuable knowledge for geo-targeted advertising.  ...  Localized association rule mining [1] takes a similar approach to ours, but it discovers association rules that hold in local clustered basket data, and is limited to non-spatial basket datasets.  ... 
doi:10.1109/icsdm.2011.5968999 dblp:conf/icsdm/MillerCEB11 fatcat:ndpp2ezedbdwnffpbkjpycl6ci

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.  ...  A co-location mining framework is introduced that operates in the continuous domain and which views regional co-location mining as a clustering problem in which an externally given fitness function has  ...  This research was also supported in part by a grant from the Environmental Institute of Houston (EIH).  ... 
doi:10.1145/1463434.1463472 dblp:conf/gis/EickPDSN08 fatcat:fktkri2pwrfbbjv7yrixrxspp4


N. Naga Saranya .
2014 International Journal of Research in Engineering and Technology  
So this research offers an innovative idea to discover the trend on multi-dimensional spatio-temporal datasets. Here it briefly describes the scope and relevancy of spatiotemporal data.  ...  Spatio-temporal data is any information regarding space and time. It is frequently updated data with 1TB/hr, are greatly challenging our ability to digest the data.  ...  From the literature survey it has listed a number of issues. And also it contributes several phases, in which each and every phase output must be very helpful to go for the next phase input.  ... 
doi:10.15623/ijret.2014.0303046 fatcat:qoyuzflmbvfozgle4nmwkvl23q

Discovering spatio-social motifs of electoral support using discriminative pattern mining

Tomasz F. Stepinski, Josue Salazar, Wei Ding
2010 Proceedings of the 1st International Conference and Exhibition on Computing for Geospatial Research & Application - COM.Geo '10  
Nicot, "A Framework for Regional Association Rule Mining and Scoping in Spatial Datasets," GeoInformatica, Volume 15, Issue 1, DOI: 10.1007/s10707-010-0111-6, January, 2011.  ...  "A Framework for Regional Association Rule Mining in Spatial Datasets," Faculty Development Fund, University of Houston-Clear Lake, PI, $2,000, 2006. 19.  ...  Teaching Undergraduate Students ( (1) gone on to graduate school, (2) employed in major field, (3) still enrolled in college)  ... 
doi:10.1145/1823854.1823898 dblp:conf/comgeo/StepinskiSD10 fatcat:lt77jekqtzagtnemf7ll3mcryq

Mining Of Spatial Co-location Pattern from Spatial Datasets

G.Kiran Kumar, P.Premchand P.Premchand, T.Venu Gopal
2012 International Journal of Computer Applications  
Spatial co-location patterns associate the co-existence of non-spatial features in a spatial neighborhood.  ...  A huge amount of spatial data and newly emerging concept of Spatial Data Mining which includes the spatial distance made it an arduous task.  ...  A spatial association rule is a rule of the form "AB" where A and B are sets of predicates and some of which are spatial ones.  ... 
doi:10.5120/5836-7994 fatcat:qbpnvj327fhitapj4fwmzwjc2m

A hybrid spatial data mining approach based on fuzzy topological relations and MOSES evolutionary algorithm [article]

Amir Hossein Goudarzi, Nasser Ghadiri
2017 arXiv   pre-print
Due to the large volume of data gathered in spatial databases, and the uncertainty of spatial objects, mining association rules for high-level knowledge representation is a challenging task.  ...  In this paper, a novel approach for spatial data mining based on the MOSES evolutionary framework is presented which improves the classic genetic programming approach.  ...  We also thank Marconi de Arruda Pereira for the datasets and DMGeo source code.  ... 
arXiv:1704.06621v1 fatcat:ip4h67tmpzhivor4rtfehts2ym

Comparative Study and Analysis of Wholesale Customer's Dataset Using Association Rule Mining

Vijayakumar M
2018 International Journal for Research in Applied Science and Engineering Technology  
Association Rule Mining (ARM) has always been the area of interest for many researchers for a long time and continues to be the same. It is one of the important tasks of the data mining concept.  ...  In this paper, the association rule mining algorithms namely Apriori, Predictive Apriori and Filtered Associator is being implemented in the Whole sale customer's dataset and the performance of these algorithms  ...  The Association rule is one of the best-known data mining techniques. In association rules, a pattern is discovered based on a relationship between items in the same transaction.  ... 
doi:10.22214/ijraset.2018.1270 fatcat:73as2pvgcjftdp3jl7flwtx7ye

Applications of FP-Growth and Apriori Algorithm for Mining Fuzzified Spatial Dataset

2019 International Journal of Engineering and Advanced Technology  
This paper provides the method for finding fuzzy spatial data of association rule. Association rules provided valuable data in the assessment of important correlations observed in big databases.  ...  Compared to the previous research work, the current approach for there search highlights the superiority over the same dataset in terms of time taken and generated rules.  ...  In this paper [12] , Incremental topological association rules for the mining of spatial datasets are suggested using probabilistic methodology.  ... 
doi:10.35940/ijeat.b3866.129219 fatcat:47tnoqilw5htjehecncepqmqke

Mining frequent geographic patterns with knowledge constraints

Vania Bogorny, Sandro Camargo, Paulo Martins Engel, Luis Otavio Alvares
2006 Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems - GIS '06  
This paper presents a two step method for mining frequent geographic patterns without associations that are previously known as non-interesting.  ...  In geographic pattern mining, besides the large amount of patterns, many are well known geographic domain associations.  ...  A FRAMEWORK FOR MINING FREQUENT GEOGRAPHIC PATTERNS WITH KNOWLEDGE CONSTRAINTS Aiming to provide a complete and integrated framework for frequent geographic pattern mining without well known associations  ... 
doi:10.1145/1183471.1183495 dblp:conf/gis/BogornyCEA06 fatcat:5ieutbvwhzd7fdce5outarg5am

SAS: Implementation of scaled association rules on spatial multidimensional quantitative dataset

M. N., Sapna Jain, M Afshar
2012 International Journal of Advanced Computer Science and Applications  
Algorithms for mining spatial association rules are similar to association rule mining except consideration of special data, the predicates generation and rule generation processes are based on Apriori  ...  Mining spatial association rules is one of the most important branches in the field of Spatial Data Mining (SDM).  ...  RELATED WORK Spatial datasets need to be preprocessed to construct the transaction database before mining spatial association rules according to the main idea of mining spatial association rules at present  ... 
doi:10.14569/ijacsa.2012.030919 fatcat:hgog3euaofcupnrsfhtnvmkl44

An Improved Intelligent Agent for Mining Real-Time Databases Using Modified Cortical Learning Algorithms [article]

N.E. Osegi
2016 arXiv   pre-print
In this paper, we present a first step in realising useful HTM like applications specifically for mining a synthetic and real time dataset based on a novel intelligent agent framework, and demonstrate  ...  Currently, intelligent agents are embedded in almost every modern day electronic system found in homes, offices and industries worldwide.  ...  We put forward a new model of Cortical Learning based on the First-Last Rule (FLR) and the Frequent-Occurring Rules (FOR).  ... 
arXiv:1601.00191v1 fatcat:ond7ncojsbeqtap77jxfjuvore

Identifying and Analyzing the Prevalent Regions of a Co-Location Pattern Using Polygons Clustering Approach

Wenhao Yu
2017 ISPRS International Journal of Geo-Information  
Given a co-location pattern consisting of spatial features, the prevalent region mining process identifies local areas in which these features are co-located with a high probability.  ...  However, traditionally, most of the solutions focus on itemsets mining and results outputting in a textual format, which fail to adequately treat all the spatial nature of the underlying entities and processes  ...  Essentially, spatial co-location mining belongs to the domain of association rule mining [14, 15] . It improves the transaction based approaches by incorporating the concept of spatial proximity.  ... 
doi:10.3390/ijgi6090259 fatcat:cp6aqu3lgbghdpnydnclazx5yi

An Overview of Object-Oriented Frameworks with Application in Spatio temporal Data Mining

K. Venkateswara Rao, A. Govardhan, K.V. Chalapati Rao
2011 CVR Journal of Science and Technology  
The main focus of this paper is to provide an overview of object oriented frameworks, capturing requirements, analysis and design of objectoriented framework for spatiotemporal data mining.  ...  So there is a need to develop a framework that takes care of common requirements at analysis and design level so that spatiotemporal data mining applications can be built through software reuse.  ...  For example, "soil erosion" is a property of space organized in a layer, representing sets of regions (with different values).  ... 
doi:10.32377/cvrjst0102 fatcat:2cryaepyzvderidxjfxopqcz4q
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