This paper describes text mining technique for automatically extracting association rules from collections of textual documents. The technique called, Extracting Association Rules from Text (EART). It depends on keyword features for discover association rules amongst keywords labeling the documents. In this work, the EART system ignores the order in which the words occur, but instead focusing o...

-In association rules mining application, some rules can provide a lot of useful knowledge for us, though these rules have the lower Support, called weak-support mode in this paper. However, in existing Support-Confidence framework, the rules with lower Support will be lost. Thus, this paper puts forward a new association rules mining technique, which sets up the lower support threshold value t...

-Data mining includes number of techniques like clustering, classification, sequential patterns, association rules and etc. Association rule mining is a technique for mining interesting rules from large databases for further analysis. Association rule mining includes two-step approach for extracting the rules. These two steps require many database scan and use support and confidence as the thre...

The Transactions in web data often consist of quantitative data, suggesting that fuzzy set theory can be used to represent such data. The time spent by users on each web page is one type of web data, was regarded as a trapezoidal membership function (TMF) and can be used to evaluate user browsing behavior. The quality of mining fuzzy association rules depends on membership functions and since t...

Association rules mining is one of the most successfully applied data mining methods in today’s business settings (e.g. Amazon or Netflix recommendations to customers). Qualified association rules mining is an extension of the association rules data mining method, that uncovers previously unknown correlations that only manifest themselves under certain circumstances (e.g. on a particular day of...

In this paper, data mining of association rules, data mining of association rules on distributed databases and distributed encrpytion techniques are introduced. Secondly, existing algorithms of data mining of association rules on distributed databases are analyzed in detail, and then they are improved on aspects of efficiency and security, whereafter the algorithm of EP_ DMA is proposed, later ...

Article history: Received 14 September 2008 Received in revised form 5 March 2010 Accepted 8 March 2010 Available online 15 March 2010 This paper presents the concept of temporal association rules in order to solve the problem of handling time series by including time expressions into association rules. Actually, temporal databases are continually appended or updated so that the discovered rule...

Association rule mining is one of the most popular data mining techniques. Significant work has been done to extend the basic association rule framework to allow for mining rules with negation. Negative association rules indicate the presence of negative correlation between items and can reveal valuable knowledge about examined dataset. Unfortunately, the sparsity of the input data significantl...

Association rules are interesting correlations among attributes in a database. These rules have many applications in areas ranging from e-commerce to sports to census analysis to medical diagnosis. The discovery of association rules is an extremely computationally expensive task and it is therefore imperative to have fast scalable algorithms for mining these rules. In this thesis, we present eÆ...

Frequent itemset mining and association rule generation is a challenging task in data stream. Even though, various algorithms have been proposed to solve the issue, it has been found out that only frequency does not decides the significance interestingness of the mined itemset and hence the association rules. This accelerates the algorithms to mine the association rules based on utility i.e. pr...