A Database Interface for Clustering in Large Spatial Databases

Keywords

Database, Computer science, Spatial database, Cluster analysis, Data mining

References

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• Efficient and Effective Clustering Methods for Spatial Data MiningRaymond T. Ng, Jiawei Han — 1994
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• This Is a Publication of the American Association for Artificial Intelligence

Cited by

• Very Fast Outlier Detection in Large Multidimensional Data SetsAmitabh Chaudhary, Alexander S. Szalay, Andrew Moore — 2002
• Self Organizing Sensors by Minimization of Cluster Heads Using Intelligent ClusteringKwangcheol Shin, Ajith Abraham, Sang Yong Han — 2006
• Direct Manipulation Querying of Database SystemsH. V. Jagadish, Bin Liu — 2011
• Consensus and analia: new challenges in detection and management of security vulnerabilities in data networksGuiomar Corral i Torruella — 2009
• Density-Connected Sets and their Application for Trend Detection in Spatial DatabasesMartin Ester, Hans‐Peter Kriegel, Jörg Sander — 1997
• A descoberta de conhecimento em bases de dados geográficas através da explicitação semânticaMaribel Yasmina Santos, Luís Amaral, Pedro Pimenta — 1999
• Perimeter Clustering Algorithm to Reduce the Number of IterationsCh. Satyanarayana, V. Vijaya Kumar — 2011
• Scalable and practical probability density estimators for scientific anomaly detectionDan Pelleg, Andrew Moore — 2004
• Scaling Clustering Algorithms to Large DatabasesPatricia Bradley, Usama M. Fayyad, Cory Reina — 1998
• A qualitative spatial reasoning approach in knowledge discovery in spatial databasesMaribel Yasmina Santos, Luís Amaral — 2000
• Detecting Current Outliers: Continuous Outlier Detection over Time-Series Data StreamsKozue Ishida, Hiroyuki Kitagawa — 2008
• Etude comportementale des mesures d'intérêt d'extraction de connaissances. (Behavioral study of interestingness measures of knowledge extraction)Dhouha Grissa — 2013
• Advances in Databases and Information Systems: Third East European Conference, ADBIS’99 Maribor, Slovenia, September 13–16, 1999 ProceedingsJohann Eder, Ivan Rozman, Tatjana Welzer — 1999
• Learning Simple Relations: Theory and ApplicationsPavel Berkhin, Jonathan D. Becher — 2002
• CURIO: A Fast Outlier and Outlier Cluster Detection Algorithm for Large DatasetsAaron Ceglar, John F. Roddick, David Powers — 2007
• A Fast Parallel Clustering Algorithm for Large Spatial DatabasesXiaowei Xu, Jochen Jäger, Hans‐Peter Kriegel — 1999
• Data mining and personalization technologiesPhilip S. Yu — 1999
• X-means: Extending K-means with Efficient Estimation of the Number of ClustersDan Pelleg, Andrew Moore — 2000
• CURIO : A Fast Outlier Clustering Algorithm for Large Datasets∗Aaron Ceglar, John F. Roddick, David Powers — 2006
• Clustering sequences of categorical valuesTadeusz Morzy, Marek Wojciechowski, Maciej Zakrzewicz — 2000

Related papers

• BIRCH: an efficient data clustering method for very large databasesTian Zhang, Raghu Ramakrishnan, Miron Livny — 1996
• Efficient and Effective Clustering Methods for Spatial Data MiningRaymond T. Ng, Jiawei Han — 1994
• A density-based algorithm for discovering clusters in large spatial Databases with NoiseMartin Ester, Hans‐Peter Kriegel, Jörg Sander — 1996
• Automatic subspace clustering of high dimensional data for data mining applicationsRakesh Agrawal, Johannes Gehrke, Dimitrios Gunopulos — 1998
• Algorithms for Clustering DataWarren S. Sarle, Anil K. Jain, Richard C. Dubes — 1988
• The R*-tree: an efficient and robust access method for points and rectanglesNorbert Beckmann, Hans‐Peter Kriegel, Ralf Schneider — 1990
• Algorithms for Mining Distance-Based Outliers in Large DatasetsEdwin M. Knorr, Raymond T. Ng — 1998