Efficient and Effective Clustering Methods for Spatial Data Mining

Keywords

Cluster analysis, Data mining, Computer science, Spatial analysis, CURE data clustering algorithm

References

• An Interval Classifier for Database Mining ApplicationsRakesh Agrawal, Sakti P. Ghosh, Tomasz Imieliński — 1992
• Finding Groups in Data: An Introduction to Cluster AnalysisLeonard Kaufman, Peter J. Rousseeuw — 1990
• Optimization for Spatial Query ProcessingWalid G. Aref, Hanan Samet — 1991
• Fundamentals of spatial information systemsR. Laurini, Derek Thompson — 1992
• Discovery of General Knowledge in Large Spatial DatabasesWei Lu, Jungang Han, Ooi B C — 1993
• Knowledge Discovery in Databases: An Attribute-Oriented ApproachJiawei Han, Yandong Cai, Nick Cercone — 1992
• Supporting data mining of large databases by visual feedback queriesDaniel A. Keim, H.-P. Kriegel, Thomas Seidl — 1994
• An examination of procedures for determining the number of clusters in a data setGlenn W. Milligan, Martha C. Cooper — 1985
• Randomized algorithms for optimizing large join queriesYannis Ioannidis, Younkyung Cha Kang — 1990
• Efficient processing of spatial joins using R-treesThomas Brinkhoff, Hans‐Peter Kriegel, Bernhard Seeger — 1993
• Query optimization by simulated annealingYannis Ioannidis, Eugene Wong — 1987
• Efficient computation of spatial joinsOliver Günther — 1993
• Loading data into description reasonersAlex Borgida, Ronald J. Brachman — 1993
• Mining association rules between sets of items in large databasesRakesh Agrawal, Tomasz Imieliński, Arun Swami — 1993
• Cluster Dissection and Analysis: Theory, Fortran Programs, Examples.Fionn Murtagh, H. Späth — 1986
• Knowledge Discovery in DatabasesJ FrawleyWilliam, Piatetsky-ShapiroGregory, J MatheusChristopher — 1991
• The Design and Analysis of Spatial Data StructuresHanan Samet — 1989
• Knowledge Discovery in DatabasesGregory Piateski, William Frawley — 1991

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• Apprentissage automatique pour l'extraction de caractéristiques : application au partitionnement de documents, au résumé automatique et au filtrage collaboratifJean-François Pessiot — 2008
• A Local Search Approach to K-ClusteringBin Zhang, Gary Kleyner, Meichun Hsu — 1999
• Generalized Density-Based Clustering for Spatial Data Mining (Abstract)Jörg Sander — 1999
• Density-based clustering in large databases using projections and visualizationsAlexander Hinneburg — 2002
• Discovery Visualization and Visual Data MiningWilliam Ribarsky, Jochen Katz, Frank Jiang — 1999
• gCLUTO – An Interactive Clustering, Visualization, and Analysis SystemMatthew D. Rasmussen, George Karypis — 2004
• Multiple Task Allocation Problems with Team FormationWenge Guo, Kendall E. Nygard, Haiyan Qiao — 2002
• A Cluster Analysis Framework using DiMaxL - Clustering and data reduction in the presence of noisePhilip Werner — 2010
• Strategic selection of chemicals for testing. Part I. Functionalities and performance of basic selection methodsJiří Knížek, Jan Šindelář, Zdenek Pulpan — 2009
• Very Fast Outlier Detection in Large Multidimensional Data SetsAmitabh Chaudhary, Alexander S. Szalay, Andrew Moore — 2002
• Multivariate Clustering of Large-Scale Scientific Simulation DataTina Eliassi‐Rad, Terence Critchlow — 2003
• K-automatic discovery in large image databasesGregory Fernandez, Philippe Peter, Abdelouaheb Mekaouche — 2002
• Consolidation of business process model collectionsChathura C. Ekanayake — 2014
• A visual framework to accelerate knowledge discovery based on dimensionality reduction minimizing degradation of qualityAntonio Gracia Berná — 2014
• Evaluating Subspace Clustering AlgorithmsLance Parsons, Ehtesham Haque, Huan Liu — 2004
• Data Visualization in RDBMSMaria Camila N. Barioni, Elisângela Botelho, Christos Faloutsos — 2002
• Ad Hoc Query Support For Very Large Simulation Mesh Data: The Metadata ApproachB. S. Lee, Robert R. Snapp, Ron Musick — 2001
• An Approach to Text Mining using Information ExtractionHaralampos Karanikas, Christos Tjortjis — 2000
• Hierarchical Clustering Based on K-Means as Local Sample(HCKM)Ahmed Fahim, Abdel-Badeeh M. Salem, Fawzy A. Torkey — 2007
• Self Organizing Sensors by Minimization of Cluster Heads Using Intelligent ClusteringKwangcheol Shin, Ajith Abraham, Sang Yong Han — 2006

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