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State of Art of Multi Relational Data Mining Approaches: A Rule Mining Algorithm

by Neelamadhab Padhy, M. Kannan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 64 - Number 16
Year of Publication: 2013
Authors: Neelamadhab Padhy, M. Kannan
10.5120/10719-5485

Neelamadhab Padhy, M. Kannan . State of Art of Multi Relational Data Mining Approaches: A Rule Mining Algorithm. International Journal of Computer Applications. 64, 16 ( February 2013), 29-39. DOI=10.5120/10719-5485

@article{ 10.5120/10719-5485,
author = { Neelamadhab Padhy, M. Kannan },
title = { State of Art of Multi Relational Data Mining Approaches: A Rule Mining Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { February 2013 },
volume = { 64 },
number = { 16 },
month = { February },
year = { 2013 },
issn = { 0975-8887 },
pages = { 29-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume64/number16/10719-5485/ },
doi = { 10.5120/10719-5485 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:16:37.960244+05:30
%A Neelamadhab Padhy
%A M. Kannan
%T State of Art of Multi Relational Data Mining Approaches: A Rule Mining Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 64
%N 16
%P 29-39
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this 21st century is completely called as the information science where the large organizations need useful knowledge. The data mining algorithms look for patterns in data. While most existing data mining approaches look for patterns in a single data table, multi-relational data mining (MRDM) approaches look for patterns that involve multiple tables (relations) from a relational database. The database consists of a collection of tables (a relational database). Records in each table represent parts, and individuals can be reconstructed by joining over the foreign key relations between the tables. To reduce the I/O cost, the data accessed together during extraction phase are to be clustered in the same disk block. This paper represents the index structure ,what we generally called as the Imine index structure . This structure can efficiently exploited by different item set extraction as well as this novel index structure is implemented by using FP-Growth and LCM V. 2 algorithms. Again in this paper we have focused that how the MRDM techniques are used in different approaches like classification, clustering ILP (Inductive Logic Program) etc.

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Index Terms

Computer Science
Information Sciences

Keywords

Multi-relational Data Mining Association rules frequent item sets mining Structured Data Mining Rule mining Algorithm in MRDM(FP-Tree LCM V. 2 )

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