Fuzzy rules, doubtlessly one of the most powerful tools of fuzzy logic, have not only been used successfully in established application areas like control engineering and approximate reasoning, but more recently also in the field of data mining. In this chapter, we provide a synthesis of different approaches to fuzzy association analysis, that is, the data-driven extraction of interesting patte...

A fuzzy association rule algorithm is implemented to extract the relationship between the atmospheric indices and the Indian Summer Monsoon Rainfall (ISMR). ENSO and EQWIN indices are used as the causative variables. Rules extracted are showing a negative relationship with ENSO index and a positive relationship with the EQWIN index. A fuzzy rule based prediction technique is also implemented on...

In this paper we propose an effective and efficient new Fuzzy Healthy Association Rule Mining Algorithm (FHARM) that produces more interesting and quality rules by introducing new quality measures. In this approach, edible attributes are filtered from transactional input data by projections and are then converted to Required Daily Allowance (RDA) numeric values. The averaged RDA database is the...

Fuzzy rule-based systems are nowadays one of the most successful applications of fuzzy logic, but in complex applications with a large set of variables, the number of rules increases exponentially and the obtained fuzzy system is scarcely interpretable. Hierarchical fuzzy systems are one of the alternatives presented in the literature to overcome this problem. This paper presents a multilevel f...

Given a donor database by a charitable organization in Hong Kong, we propose to use a new data mining technique to discover fuzzy rules for direct marketing. The discovered fuzzy rules employ linguistic terms, which are natural for human users to understand because of the affinity with the human knowledge representations, to represent the association relationships revealed in the data. The prop...

⎯This paper presents a new approach for solving the paradoxical Blackman's Association Problem. It utilizes the recently defined new class fusion rule based on fuzzy Tconorm/T-norm operators together with DezertSmarandache theory based, relative variations of generalized pignistic probabilities measure of correct associations, defined from a partial ordering function of hyper-power set. The abi...

Nowadays, discovery the association rules is an important and controversial area in data mining research studies. These rules, describe noticeable association relationships among different attributes. While most studies have focused on binary valued transaction data, in real world applications, there data usually consist of quantitative values. With that in mind, in this paper, we propose a fuz...

The aim of this paper is to provide a crystal clear insight into the true semantics of the measures of support and confidence that are used to assess rule quality in fuzzy association rule mining. To achieve this, we rely on two important pillars: the identification of transactions in a database as positive or negative examples of a given association between attributes, and the correspondence b...

Association rules mining from temporal dataset is to find associations between items that hold within certain time frame but not throughout the dataset. This problem involves first discovering frequent itemsets which are frequent at certain time intervals and then extracting association rules from such frequent itemsets. In practice, we may have datasets having imprecise or fuzzy time attribute...

Fuzzy mining approaches have recently been discussed for deriving fuzzy knowledge. Since items may have their own characteristics, different minimum supports and membership functions may be specified for different items. In the past, we proposed a genetic-fuzzy data-mining algorithm for extracting minimum supports and membership functions for items from quantitative transactions. In that paper,...