Open AccessGenerating Non-redundant Multilevel Association Rules Using Min-max Exact Rules
Author(s) -
R. Vijaya Prakash,
Samar Sen Sarma,
M. Sheshikala
Publication year - 2018
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v8i6.pp4568-4576
Subject(s) - association rule learning , redundancy (engineering) , data mining , computer science , representation (politics) , basis (linear algebra) , association (psychology) , field (mathematics) , k optimal pattern discovery , machine learning , mathematics , philosophy , geometry , epistemology , politics , political science , pure mathematics , law , operating system
Association Rule mining plays an important role in the discovery of knowledge and information. Association Rule mining discovers huge number of rules for any dataset for different support and confidence values, among this many of them are redundant, especially in the case of multi-level datasets. Mining non-redundant Association Rules in multi-level dataset is a big concern in field of Data mining. In this paper, we present a definition for redundancy and a concise representation called Reliable Exact basis for representing non-redundant Association Rules from multi-level datasets. The given non-redundant Association Rules are loss less representation for any datasets.