This paper is two folded. In first fold, the authors have illustrated the interplay among fuzzy, rough, and soft set theory and their way of handling vagueness. In second fold, the authors have studied their individual strengths to discover association rules. The performance of these three approaches in discovering comprehensible rules are presented. Usage of Fuzzy, Rough, and Soft Set Approach...

Association rule mainly focuses on large transactional databases. In association rule mining all items are considered with equal weightage. But it is not suitable for all datasets. The weight should be considered based on the importance of the item. In our previous work HITS algorithm (Hyperlink Induced Topic Search) is used to find the weight of an item w-support is calculated for generating f...

Mining association rules is one of the important tasks in the process of data mining application. In general, the input as used in the process of generating rules is taken from a certain data table by which all the corresponding values of every domain data have correlations one to each others as given in the table. A problem arises when we need to generate the rules expressing the relationship ...

In this paper we present an algorithm for extracting fuzzy association rules between weighted keyphrases in collections of text documents. First, we discuss some classical approaches to association rule extraction and then we show the fuzzy association rules algorithm. The proposed method integrates the fuzzy set concept and the apriori algorithm. The algorithm emphasizes the distinction betwee...

Data Mining is most commonly used in attempts to induce association rules from databases which can help decision-makers easily analyze the data and make good decisions regarding the domains concerned. Different studies have proposed methods for mining association rules from databases with crisp values. However, the data in many real-world applications consist of interval and fuzzy values. In th...

Data mining is most commonly used in attempts to induce association rules from transaction data. Transactions in real-world applications, however, usually consist of quantitative values. This paper thus proposes a fuzzy data-mining algorithm for extracting both association rules and membership functions from quantitative transactions. We present a GA-based framework for finding membership funct...

Data mining is new but an interdisciplinary field utilizing statistics, machine learning, and other methods. In recent years, fuzzy logic has also been applied to augment data mining. The application of fuzzy logics makes the mining results more understandable and interpretable, apart from being useful and informative. Fuzzy rules are useful to summarize large databases. Several studies are don...

Information retrieval (IR) focuses on the process of determining and assessing the adequacy between a user-query and a collection of documents, yielding a subset of relevant documents. In this respect, query expansion aims to reduce an eventual query/document mismatch by expanding the query using ”correlated” terms. In this paper, we present an approach based on the use of association rules to ...

Preliminary studies on data mining focus on finding association rules from transaction databases containing items without relationships among them. However, relationships among items often exist in real applications. Most of the previous works only concern about Is-A hierarchy. In this paper, hierarchical relationships include a Has-A hierarchy and multiple Is-A hierarchies are discussed. The p...