نتایج جستجو برای: fuzzy association rules

تعداد نتایج: 706454  

Journal: :TELKOMNIKA (Telecommunication Computing Electronics and Control) 2019

Journal: :J. Network and Computer Applications 2007
Tansel Özyer Reda Alhajj Ken Barker

The purpose of the work described in this paper is to provide an intelligent intrusion detection system (IIDS) that uses two of the most popular data mining tasks, namely classification and association rules mining together for predicting different behaviors in networked computers. To achieve this, we propose a method based on iterative rule learning using a fuzzy rule-based genetic classifier....

Journal: :journal of ai and data mining 2014
farzaneh zahedi mohammad-reza zare-mirakabad

drug addiction is a major social, economic, and hygienic challenge that impacts on all the community and needs serious threat. available treatments are successful only in short-term unless underlying reasons making individuals prone to the phenomenon are not investigated. nowadays, there are some treatment centers which have comprehensive information about addicted people. therefore, given the ...

2006
Céline Fiot Anne Laurent Maguelonne Teisseire Bénédicte Laurent

Mining fuzzy rules is one of the best ways to summarize large databases while keeping information as clear and understandable as possible for the end-user. Several approaches have been proposed to mine such fuzzy rules, in particular to mine fuzzy association rules. However, we argue that it is important to mine rules that convey information about the order. For instance, it is very interesting...

2009
Rolly Intan Oviliani Yenty Yuliana

Decision Tree Induction (DTI), one of the Data Mining classification methods, is used in this research for predictive problem solving in analyzing patient medical track records. In this paper, we extend the concept of DTI dealing with meaningful fuzzy labels in order to express human knowledge for mining fuzzy association rules. Meaningful fuzzy labels (using fuzzy sets) can be defined for each...

2005
George Stephanides Mihai Gabroveanu Mirel Cosulschi Nicolae Constantinescu

Data mining, also known as knowledge discovery in databases, is the process of discovery potentially useful, hidden knowledge or relations among data from large databases. An important topic in data mining research is concerned with the discovery of association rules. The majority of databases are distributed nowadays. In this paper is presented an algorithm for mining fuzzy association rules f...

2000
ATTILA GYENESEI

The problem of mining association rules for fuzzy quantitative items was introduced and an algorithm proposed in [5]. However, the algorithm assumes that fuzzy sets are given. In this paper we propose a method to find the fuzzy sets for each quantitative attribute in a database by using clustering techniques. We present a scheme for finding the optimal partitioning of a data set during the clus...

2004
Didier Dubois Eyke Hüllermeier Henri Prade

In order to allow for the analysis of data sets including numerical attributes, several generalizations of association rule mining based on fuzzy sets have been proposed in the literature. While the formal specification of fuzzy associations is more or less straightforward, the assessment of such rules by means of appropriate quality measures is less obvious. Particularly, it assumes an underst...

2012
Nikhat Fatma Shaikh Jagdish W Bakal Madhu Nashipudimath Chun-Hao Chen Tzung-Pei Hong T. P. Hong C. H. Chen Y. L. Wu Hung-Pin Chiu Yi-Tsung Tang Chan-Sheng Kuo Sheng-Chai Chi Sulaiman Khan Maybin Muyeba Frans Coenen Miguel Delgado Nicolás Marín Daniel Sánchez Li-Huei Tseng Ming-Jer Chiang Shyue-Liang Wang

Data mining of association rules from items in transaction databases has been studied extensively in recent years. However these algorithms deal with only transactions with binary values whereas transactions with quantitative values are more commonly seen in real-world applications. As to fuzzy data mining, many approaches have also been proposed for mining fuzzy association rules. Most of the ...

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