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Reseach Article

Analysis of Sequential Mining Algorithms

by Surbhi Chandhok, Romil Anand, Soumay Gupta, Aatif Jamshed
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 165 - Number 12
Year of Publication: 2017
Authors: Surbhi Chandhok, Romil Anand, Soumay Gupta, Aatif Jamshed
10.5120/ijca2017914085

Surbhi Chandhok, Romil Anand, Soumay Gupta, Aatif Jamshed . Analysis of Sequential Mining Algorithms. International Journal of Computer Applications. 165, 12 ( May 2017), 14-16. DOI=10.5120/ijca2017914085

@article{ 10.5120/ijca2017914085,
author = { Surbhi Chandhok, Romil Anand, Soumay Gupta, Aatif Jamshed },
title = { Analysis of Sequential Mining Algorithms },
journal = { International Journal of Computer Applications },
issue_date = { May 2017 },
volume = { 165 },
number = { 12 },
month = { May },
year = { 2017 },
issn = { 0975-8887 },
pages = { 14-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume165/number12/27624-2017914085/ },
doi = { 10.5120/ijca2017914085 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:12:17.463055+05:30
%A Surbhi Chandhok
%A Romil Anand
%A Soumay Gupta
%A Aatif Jamshed
%T Analysis of Sequential Mining Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 165
%N 12
%P 14-16
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper essentially analyses the sequential pattern of mining algorithms. The discovery of Association relationship seeks more attention in data mining due to the constantly increasing amount of data stored in the real application system. Mining for association rules has its usage in several areas of business such as the process of decision making and the development of customized marketing programs & strategies. Therefore, the primary objective of data mining is to transform “data into knowledge”. As a result of which, mining association rules from enormous databases has been a significant topic in recent research for knowledge discovery in databases. It is known that database can be both dynamic and static. Static databases are the ones that do not change or alter with the passage of time. On the other hand, in dynamic databases, various new transactions append as time passes by. This might result in the production of some new itemsets while it is possible that certain frequent itemsets might as well become invalid. Therefore, in dynamic databases, the maintenance of large itemsets can be extremely expensive, in case rerun of previous mining algorithms on updated database is applied as it repeats a major portion of work done during previous computations. Apart from this, there is also lack of space for the storage of all the data and its processing. Therefore, it is recommended that instead of finding enormous itemsets again, certain heuristics be used for mining of dynamic databases. It brings forth the study of sequential pattern- mining algorithms, classified into five varied classes. 1. on the basis of Apriori-based algorithm. 2. on the basis of FP-Growth Algorithm. 3. on the bassis of Fast Algorithm. 4. on Partition Based Algorithm. 5. on the basis of Fast Update algorithm.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Sequential Pattern Data Mining Pattern analysis