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On the Use of Rough Set Theory for Mining Periodic Frequent Patterns
Author(s) -
Manjeet Samoliya,
Akhilesh Tiwari
Publication year - 2016
Publication title -
international journal of information technology and computer science
Language(s) - English
Resource type - Journals
eISSN - 2074-9015
pISSN - 2074-9007
DOI - 10.5815/ijitcs.2016.07.08
Subject(s) - computer science , rough set , representation (politics) , profitability index , apriori algorithm , set (abstract data type) , a priori and a posteriori , data mining , process (computing) , order (exchange) , data science , operations research , association rule learning , mathematics , business , philosophy , finance , epistemology , politics , political science , law , programming language , operating system
This paper presents a new Apriori based\udapproach for mining periodic frequent patterns from the\udtemporal database. The proposed approach utilizes the\udconcept of rough set theory for obtaining reduced\udrepresentation of the initially considered temporal\uddatabase. In order to consider only the relevant items for\udanalyzing seasonal effects, a decision attribute festival\udhas been considered. It has been observed that the\udproposed approach works fine for the analysis of the\udseasonal impact on buying behavior of customers.\udConsidering the capability of approach for the analysis of\udseasonal profitability concern, decision making, and\udfuture marketing may use it for the important decisionmaking\udprocess for the uplifting of sel

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