Fuzzy Newton-Cotes method for integration of fuzzy functions that was proposed by Ahmady in [1]. In this paper we construct error estimate of fuzzy Newton-Cotes method such as fuzzy Trapezoidal rule and fuzzy Simpson rule by using Taylor's series. The corresponding error terms are proven by two theorems. We prove that the fuzzy Trapezoidal rule is accurate for fuzzy polynomial of degree one and...

In this paper, we present an application of association rules to query re#nement. Starting from an initial set of documents retrieved from the web, text transactions are constructed and association rules are extracted. A fuzzy extension of text transactions and association rules is employed, where the presence of the terms (items) in the documents (transactions) is determined with a value betwe...

The goal of this paper is to present a logic model with very good properties for the representation and evaluation of fuzzy association rules. For that we are going to combine two approaches. The first one is a model for the representation and evaluation of crisp association rules. The second one is a new proposal for the representation of imprecise properties (in particular for fuzzy sets) by ...

One of the most well-known and extended data mining techniques is that of association rule mining, a helpful tool to discover relations between items present in sets of transactions. Nevertheless, in some other scenarios, another interesting issue is that of considering not only the possible relations involving presence of items, but the absence of them. The problem gets more complex when it is...

We develop techniques for discovering patterns with periodicity in this work. Patterns with periodicity are those that occur at regular time intervals, and therefore there are two aspects to the problem: finding the pattern, and determining the periodicity. The difficulty of the task lies in the problem of discovering these regular time intervals, i.e., the periodicity. Periodicities in the dat...

In this paper we extend the problem of classification using Fuzzy Association Rule Mining and propose the concept of Fuzzy Weighted Associative Classifier (FWAC). Classification based on Association rules is considered to be effective and advantageous in many cases. Associative classifiers are especially fit to applications where the model may assist the domain experts in their decisions. Weigh...

This paper introduces a new algorithm called User Association Rules Mining (UARM) for solving the problem of generating inadequate large number of rules in mining association technique using a fuzzy logic method [1, 2]. In order to avoid user’s defined threshold mistakes, the user has flexibility to determine constraints based on a set of features. In comparison with other well-known and widely...

This article proposes an algorithm for data mining that presents a new measure for assistance in the extraction of knowledge. The algorithm uses association rules to extract rules from the databases and fuzzy logic for the classification and comparison of the collected rules. Key-words: data mining, association rules, fuzzy logic, similarity and algorithm of the inverse confidence.

The association rules, discovered by traditional support–confidence based algorithms, provide us with concise statements of potentially useful information hidden in databases. However, only considering the constraints of minimum support and minimum confidence is far from satisfying in many cases. In this paper, we propose a fuzzy method to formulate how interesting an association rule may be. I...

Mining hybrid dimension fuzzy association rule is one of the important processes in data mining . Apriori algorithm concerned with handling single level, single dimensional association rules. this paper is presenting, a new modification in joining process to reduce the redundant generation of sub items during pruning the candidate itemsets, which can obtain higher efficiency of mining that of o...