Towards Elimination of Redundant and Well Known Patterns in Spatial Association Rule Mining

In this paper we present a new method for mining spatial association rules from geographic databases. On the contrary of most existing approaches that propose syntactic constraints to reduce the number of rules, we propose to use background geographic information extracted from geographic database schemas. In a first step we remove all well known dependences explicitly represented in geographic database schemas. In a second step we remove redundant frequent sets. Experiments show a very significant reduction of the number of rules when both well known dependences and redundant frequent sets are removed.

[1]  Steven Barker.  1st International Conference on Computational Logic , 2000 .

[2]  Jiawei Han,et al.  Discovery of Spatial Association Rules in Geographic Information Databases , 1995, SSD.

[3]  James H. Cross,et al.  Reverse engineering and design recovery: a taxonomy , 1990, IEEE Software.

[4]  Abraham Silberschatz,et al.  What Makes Patterns Interesting in Knowledge Discovery Systems , 1996, IEEE Trans. Knowl. Data Eng..

[5]  Jaideep Srivastava,et al.  Selecting the right interestingness measure for association patterns , 2002, KDD.

[6]  Peretz Shoval,et al.  Database Reverse Engineering: From the Relational to the Binary Relationship model , 1993, Data Knowl. Eng..

[7]  Gerd Stumme,et al.  Mining Minimal Non-redundant Association Rules Using Frequent Closed Itemsets , 2000, Computational Logic.

[8]  Ramakrishnan Srikant,et al.  Mining generalized association rules , 1995, Future Gener. Comput. Syst..

[9]  Jun Wei Liu,et al.  Mining Association Rules in Spatio‐Temporal Data: An Analysis of Urban Socioeconomic and Land Cover Change , 2005, Trans. GIS.

[10]  Ivar Jacobson,et al.  The Unified Modeling Language User Guide , 1998, J. Database Manag..

[11]  Mohammed J. Zaki,et al.  CHARM: An Efficient Algorithm for Closed Itemset Mining , 2002, SDM.

[12]  Jian Pei,et al.  CLOSET: An Efficient Algorithm for Mining Frequent Closed Itemsets , 2000, ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery.

[13]  Ramez Elmasri,et al.  Fundamentals of Database Systems , 1989 .

[14]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[15]  Shashi Shekhar,et al.  Spatial Databases: A Tour , 2003 .

[16]  Nicolas Pasquier,et al.  Discovering Frequent Closed Itemsets for Association Rules , 1999, ICDT.

[17]  Vania Bogorny,et al.  A Reuse-based Spatial Data Preparation Framework for Data Mining , 2005, SEKE.

[18]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[19]  Stephen McKearney,et al.  Reverse Engineering Databases for Knowledge Discovery , 1996, KDD.

[20]  Stefano Spaccapietra,et al.  Modeling spatial data in the MADS conceptual model , 1998 .

[21]  Vania Bogorny,et al.  GEOARM: an Interoperable Framework to Improve Geographic Data Preprocessing and Spatial Association Rule Mining , 2006, SEKE.

[22]  Eliseo Clementini,et al.  Mining multiple-level spatial association rules for objects with a broad boundary , 2000, Data Knowl. Eng..

[23]  Alberto H. F. Laender,et al.  OMT-G: An Object-Oriented Data Model for Geographic Applications , 2001, GeoInformatica.