Abstract
Data mining is used for extracting related data. The association rules approach is one of the used methods for analyzing, discovering and extracting knowledge and mining the relationships among raw data. Commonly, it is important to understand and discover such knowledge directly from huge records of items stored in a relational database. This paper proposes an approach for generating human-like fuzzy association rules based on fuzzy ontology. It focuses on enhancing the process of extracting association rules from a huge database respecting a predefined domain fuzzy ontology. Commonly, association rules mining based on crisp ontology is found to be more flexible than classical ones as it considers the relationships between concepts or items. Yet, crisp ontology suffers from the problem of information losing resulted from the rigid boundaries of crisp relationships, which are approximated to be 0 or 1, between concepts. In contrast, the smooth boundaries of fuzzy sets make it able to represent partial relationships that range from 0 to 1 between concepts in an ontology in a more flexible human-like manner. Consequently, generating fuzzy association rules based on fuzzy ontology makes it more human-like and reliable compared with other previous ones. An illustrative case study, on two different data sets, shows the added value of the proposed approach compared with some other recent approaches.
Highlights
The increasing use of databases in different scientific and business fields resulted in huge amounts of stored data
Data mining literature has focused on the issue of developing new techniques that successfully extract information from the vast amounts of data accumulated in large databases in order to achieve the data analysis and machine learning [2]
This paper proposes an enhancement approach to extract association rules based on fuzzy ontology
Summary
The increasing use of databases in different scientific and business fields resulted in huge amounts of stored data Analysing and understanding this data are needed to extract important information by finding unsuspected relationships among observed data sets, and summarise the data to be understandable and useful to the decision makers [1]. It provides a shared and common understanding among people and systems It facilitates defining the relationships between terms and concepts in a given domain. Data mining is used to extract valuable knowledge from huge amounts of data respecting the natural relationships between the domain terms and concepts [6].
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