A Survey: On Association Rule Mining

Association Rule Mining has 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 Data mining. Association Rule Mining that used to find out correlations, association between a set of transactions is the databases and data warehouse. The knowledge obtained from these database are used for different applications like super market sales prediction, fraud detection etc. This paper presents a review on the basic concepts of ARM technique along with the recent related work that has been done in this field. Therefore this survey guides the researchers to know the progress of pattern mining using association rule for the intended purposes.

[1]  Gregory Piatetsky-Shapiro,et al.  Discovery, Analysis, and Presentation of Strong Rules , 1991, Knowledge Discovery in Databases.

[2]  Jiawei Han,et al.  Knowledge Discovery in Databases: An Attribute-Oriented Approach , 1992, VLDB.

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

[4]  Tomasz Imielinski,et al.  Database Mining: A Performance Perspective , 1993, IEEE Trans. Knowl. Data Eng..

[5]  R. Agarwal.  Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.

[6]  Viktor Jovanoski,et al.  High Confidence Association Rules for Medical Diagnosis , 1999 .

[7]  Gilbert Saporta,et al.  Data Mining and Official Statistics , 2000 .

[8]  Soung Hie Kim,et al.  Mining the change of customer behavior in an internet shopping mall , 2001, Expert Syst. Appl..

[9]  Michael J. Shaw,et al.  Knowledge management and data mining for marketing , 2001, Decis. Support Syst..

[10]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[11]  Margaret H. Dunham,et al.  Data Mining: Introductory and Advanced Topics , 2002 .

[12]  Donato Malerba,et al.  Mining spatial association rules in census data , 2002 .

[13]  Peter A. Flach,et al.  Confirmation-Guided Discovery of First-Order Rules with Tertius , 2004, Machine Learning.

[14]  Xindong Wu,et al.  Efficient mining of both positive and negative association rules , 2004, TOIS.

[15]  Ruey-Shun Chen,et al.  Data Mining Application in Customer Relationship Management of Credit Card Business , 2005, COMPSAC.

[16]  Nitin Gupta,et al.  Mining Quantitative Association Rules in Protein Sequences , 2006, Selected Papers from AusDM.

[17]  GABRIELA ŞERBAN,et al.  A PROGRAMMING INTERFACE FOR MEDICAL DIAGNOSIS PREDICTION , 2006 .

[18]  Yanyan Wu,et al.  Research and application of algorithm for mining positive and negative association rules , 2011, Proceedings of 2011 International Conference on Electronic & Mechanical Engineering and Information Technology.

[19]  Azuraliza Abu Bakar,et al.  Mining positive and Negative Association Rules from interesting frequent and infrequent itemsets , 2012, 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery.