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Multi Relational Data Mining Approaches: A Data Mining Technique

by Neelamadhab Padhy, Rasmita Panigrahi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 57 - Number 17
Year of Publication: 2012
Authors: Neelamadhab Padhy, Rasmita Panigrahi
10.5120/9207-3742

Neelamadhab Padhy, Rasmita Panigrahi . Multi Relational Data Mining Approaches: A Data Mining Technique. International Journal of Computer Applications. 57, 17 ( November 2012), 23-32. DOI=10.5120/9207-3742

@article{ 10.5120/9207-3742,
author = { Neelamadhab Padhy, Rasmita Panigrahi },
title = { Multi Relational Data Mining Approaches: A Data Mining Technique },
journal = { International Journal of Computer Applications },
issue_date = { November 2012 },
volume = { 57 },
number = { 17 },
month = { November },
year = { 2012 },
issn = { 0975-8887 },
pages = { 23-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume57/number17/9207-3742/ },
doi = { 10.5120/9207-3742 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:00:43.705621+05:30
%A Neelamadhab Padhy
%A Rasmita Panigrahi
%T Multi Relational Data Mining Approaches: A Data Mining Technique
%J International Journal of Computer Applications
%@ 0975-8887
%V 57
%N 17
%P 23-32
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The multi relational data mining approach has developed as an alternative way for handling the structured data such that RDBMS. This will provides the mining in multiple tables directly. In MRDM the patterns are available in multiple tables (relations) from a relational database. As the data are available over the many tables which will affect the many problems in the practice of the data mining. To deal with this problem, one either constructs a single table by Propositionalisation, or uses a Multi-Relational Data Mining algorithm. MRDM approaches have been successfully applied in the area of bioinformatics. Three popular pattern finding techniques classification, clustering and association are frequently used in MRDM. Multi relational approach has developed as an alternative for analyzing the structured data such as relational database. MRDM allowing applying directly in the data mining in multiple tables. To avoid the expensive joining operations and semantic losses we used the MRDM technique. This paper focuses some of the application areas of MRDM and feature directions as well as the comparison of ILP, GM, SSDM and MRDM.

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Index Terms

Computer Science
Information Sciences

Keywords

Data Mining Multi-Relational Data mining Inductive logic programming Selection graph Tuple ID propagation

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