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...

Extracting multilevel association rules in transaction databases is most commonly used tasks in data mining. This paper proposes a multilevel association rule mining using fuzzy concepts. This paper uses different fuzzy membership function to retrieve efficient association rules from multi level hierarchies that exist in a transaction dataset. In general, the data can spread into many hierarchi...

In this paper, interactions among fuzzy, rough, and soft set theory has been studied. The authors have examined these theories as a problem solving tool in association rule mining problems of data mining and knowledge discovery in databases. Although fuzzy and rough set have been well studied areas and successfully applied in association rule mining problem, but soft set theory needs more atten...

There are many various methods to forecast time series. However, there is no single forecasting method that generally outperforms any other. Consequently, there always exists a danger of choosing a method that is inappropriate for a given time series. To overcome such a problem, distinct ensemble techniques are being proposed. These techniques combine more individual forecasting methods. In thi...

Association rule mining is an important topic in data mining research. Many algorithms have been developed for such task and they typically assume that the underlying associations hidden in the data are stable over time. However, in real world domains, it is possible that the data characteristics and hence the associations change significantly over time. Existing data mining algorithms have not...

Many quality measures for rule discovery are binary measures, they are designed to rate binary rules (rules which separate the database examples into two categories eg. “it is a bird” vs. “it is not a bird”) and they cannot rate N -ary rules (rules which separate the database examples into N categories eg. “it is a bird” or “it is an insect” or “it is a fish”). Many quality measures for classif...

Association rule mining is an active data mining research area. Recent years have witnessed many efforts on discovering fuzzy associations. The key strength of fuzzy association rule mining is its completeness. This strength, however, comes with a major drawback to handle large datasets. It often produces a huge number of candidate itemsets. The huge number of candidate itemsets makes it ineffe...

The authors [2-5] have studied and presented the quantitative method of linguistic variables and linguistic threshold by fuzzy set. Chien-Hua Wang, Chin-Pang Tzong proposed an algorithms for mining fuzzy association rule [2]. In this paper, we extend the algorithms proposed in [2] for number data and linguistic variables by using hedge algebras.

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...

While traditional algorithms concern positive associations between binary or quantitative attributes of databases, this paper focuses on mining both positive and negative fuzzy association rules. We show how, by a deliberate choice of fuzzy logic connectives, significantly increased expressivity is available at little extra cost. In particular, rule quality measures for negative rules can be co...