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Reseach Article

Comparative Study and Results Analysis of Feature Vector and Adjacency Matrix for Representing FSM for Data Reduction

by Tawfiq Abdulkhaleq Abbas, Abbood Kirebut Jassim
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 110 - Number 10
Year of Publication: 2015
Authors: Tawfiq Abdulkhaleq Abbas, Abbood Kirebut Jassim
10.5120/19356-1089

Tawfiq Abdulkhaleq Abbas, Abbood Kirebut Jassim . Comparative Study and Results Analysis of Feature Vector and Adjacency Matrix for Representing FSM for Data Reduction. International Journal of Computer Applications. 110, 10 ( January 2015), 39-41. DOI=10.5120/19356-1089

@article{ 10.5120/19356-1089,
author = { Tawfiq Abdulkhaleq Abbas, Abbood Kirebut Jassim },
title = { Comparative Study and Results Analysis of Feature Vector and Adjacency Matrix for Representing FSM for Data Reduction },
journal = { International Journal of Computer Applications },
issue_date = { January 2015 },
volume = { 110 },
number = { 10 },
month = { January },
year = { 2015 },
issn = { 0975-8887 },
pages = { 39-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume110/number10/19356-1089/ },
doi = { 10.5120/19356-1089 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:46:02.808102+05:30
%A Tawfiq Abdulkhaleq Abbas
%A Abbood Kirebut Jassim
%T Comparative Study and Results Analysis of Feature Vector and Adjacency Matrix for Representing FSM for Data Reduction
%J International Journal of Computer Applications
%@ 0975-8887
%V 110
%N 10
%P 39-41
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A finite state automaton is conceptual graphs which are considered to be an important type of graph method. As a result of the expansion of using the graphs in the process of data mining, the use of FMS is still limited because of the difficulty in processing databases, therefore this paper is to find an approach that make it easier to deal with large groups of machines as a database is encourage to use of this type of representation in mining techniques. This paper gives a approach for finding a match between machines, which appear frequently in a single environment or similar environments, the approach consist of two methods one for machines matching as adjacency matrices and another method for matching machines as vectors of features ,hence prove that second method more efficient to control the match processing

References
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Index Terms

Computer Science
Information Sciences

Keywords

Features Finite State Automata Data Reduction Feature Extraction Vector Feature.