ASSOCIATION RULE MINING AND INTERESTINGNESS MEASURES: A CASE STUDY
نویسندگان
چکیده
منابع مشابه
Issues in Association Rule Mining and Interestingness
This work presents unaddressed issues in the field of Association Rule Mining (ARM). Looking at the previous literature of varied areas and applications of ARM, we identify three broad categories of ARM where the research is still in the nascent stage. We review papers in the three categories of fuzzy association rules, multilevel association rules and negative association rules to study the st...
متن کاملConfirmation measures of association rule interestingness
This paper considers advantages of measures of confirmation or evidential support in the context of interestingness of association rules. In particular, it is argued that the way in which they characterize positive/negative association has advantages over other measures such as null-invariant measures. Several properties are reviewed and proposed as requirements for an adequate confirmation mea...
متن کاملOptimized Rule Mining Through a Unified Framework for Interestingness Measures
The large amount of association rules resulting from a KDD process makes the exploitation of the patterns embedded in the database difficult even impossible. In order to address this problem, various interestingness measures were proposed for selecting the most relevant rules. Nevertheless, the choice of an appropriate measure remains a hard task and the use of several measures may lead to conf...
متن کاملOn rule interestingness measures
This paper discusses several factors influencing the evaluation of the degree of interestingness of rules discovered by a data mining algorithm. The main goals of this paper are: (1) drawing attention to several factors related to rule interestingness that have been somewhat neglected in the literature; (2) showing some ways of modifying rule interestingness measures to take these factors into ...
متن کاملItemset Size - Sensitive Interestingness Measures for Association Rule Mining and Link Prediction
Association rule learning is a data mining technique that can capture relationships between pairs of entities in different domains. The goal of this research is to discover factors from data that can improve the precision, recall, and accuracy of association rules found using interestingness measures and frequent itemset mining. Such factors can be calibrated using validation data and applied t...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Business in The Digital Age
سال: 2020
ISSN: 2651-4737
DOI: 10.46238/jobda.811464