A Multi-Methodological Approach to Rare Association Rule Mining
نویسنده
چکیده
The main goal of association rule mining is to discover relationships among sets of items in a transactional database. Association rule mining was introduced by Agrawal, Imielinski and Swami (1993). It aims to extract interesting correlations, frequent patterns, associations or causal structures among sets of items in transaction databases or other data repositories. The relationships are not based on inherent properties of the data themselves but rather based on the co-occurrence of the items within the database. The associations between items are also known as association rules. In the classiAbstrAct
منابع مشابه
Numeric Multi-Objective Rule Mining Using Simulated Annealing Algorithm
Abstract as a single objective one. Measures like support, confidence and other interestingness criteria which are used for evaluating a rule, can be thought of as different objectives of association rule mining problem. Support count is the number of records, which satisfies all the conditions that exist in the rule. This objective represents the accuracy of the rules extracted from the da...
متن کاملA Novel Method for Selecting the Supplier Based on Association Rule Mining
One of important problems in supply chains management is supplier selection. In a company, there are massive data from various departments so that extracting knowledge from the company’s data is too complicated. Many researchers have solved this problem by some methods like fuzzy set theory, goal programming, multi objective programming, the liner programming, mixed integer programming, analyti...
متن کاملA new approach based on data envelopment analysis with double frontiers for ranking the discovered rules from data mining
Data envelopment analysis (DEA) is a relatively new data oriented approach to evaluate performance of a set of peer entities called decision-making units (DMUs) that convert multiple inputs into multiple outputs. Within a relative limited period, DEA has been converted into a strong quantitative and analytical tool to measure and evaluate performance. In an article written by Toloo et al. (2009...
متن کاملOptimizing Membership Functions using Learning Automata for Fuzzy Association Rule Mining
The Transactions in web data often consist of quantitative data, suggesting that fuzzy set theory can be used to represent such data. The time spent by users on each web page is one type of web data, was regarded as a trapezoidal membership function (TMF) and can be used to evaluate user browsing behavior. The quality of mining fuzzy association rules depends on membership functions and since t...
متن کاملMulti-Objective Sentiment Analysis Using Evolutionary Algorithm for Mining Positive & Negative Association Rules
Most of the algorithms for mining Quantitative Association Rules (QAR) focuses on positive dependencies without paying particular attention to negative dependencies. The latter may be worth taking into account, however, as they relate the presence of certain items to the absence of others. The algorithms used to extract such rules usually consider only one evaluation criterion in measuring the ...
متن کامل