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Analysis and Comparative Study of Classifiers for Relational Data Mining

International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 55 - Number 7
Year of Publication: 2012
Vimalkumar B. Vaghela
Kalpesh H. Vandra
Nilesh K. Modi

Vimalkumar B Vaghela, Kalpesh H Vandra and Nilesh K Modi. Article: Analysis and Comparative Study of Classifiers for Relational Data Mining. International Journal of Computer Applications 55(7):11-21, October 2012. Full text available. BibTeX

	author = {Vimalkumar B. Vaghela and Kalpesh H. Vandra and Nilesh K. Modi},
	title = {Article: Analysis and Comparative Study of Classifiers for Relational Data Mining},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {55},
	number = {7},
	pages = {11-21},
	month = {October},
	note = {Full text available}


As an important task of relational database, relational classification can directly classify the data that involve multiple relations from a relational database and have more advantages than propositional data mining approaches. The information age has provided us with huge data repositories which cannot longer be analyzed manually. Most available existing data mining algorithms looks for pattern in a single relation. To classify data from relational database need of multi-relational classification arise which is used to analyze relational database and used to predict behavior and unknown pattern automatically which include business data, bioinformatics, pharmacology, web mining, credit card fraud detection, disease diagnosis system, computational biology, online retailers. In this paper, we present the several kinds of multi-relational classification methods including Inductive Logic Programming (ILP) based, Associative based multi-relational classification, Emerging Patterns based, Relational database based classification approaches and discuss each relational classification approaches, their characteristics, their comparisons and challenging issues in detail.


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