The disadvantages of apriori algorithm are firstly discussed. Then, a new measure of kendall-τ is proposed and treated as an interest threshold. Furthermore, an improved Apriori algorithm called K -apriori is proposed based on kendall-τ correlation coefficient. It not only can accurately find the relations between different products in transaction databases and reduce the useless rules but also...

Association rules are essential data mining tool and as such has been well researched. Many new types of association rules based on both categorial or quantitative data have been founded ([8], [7], [2], [4]). Our work is directed to the theoretical features of association rules; especially, we study a specific class of association rules called δ-cosymmetric rules. We present here some interesti...

Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. These systems provide currently relatively few structure. We discuss in this paper, how association rule mining can be adopted to analyze and structure folksonomies, and how the results can be used for ontology learning and supporting emergent semant...

In recent years, the problem of association rule mining in transactional data has been well studied. We propose to extend the discovery of classical association rules to the discovery of association rules of conjunctive queries in arbitrary relational data, inspired by the Warmr algorithm, developed by Dehaspe and Toivonen, that discovers association rules over a limited set of conjunctive quer...

EasyMiner (easyminer.eu) is a web-based association rule mining software based on the LISp-Miner system. This paper presents a proof-of-concept workflow for learning business rules with EasyMiner from transactional data. The approved rules are exported to the Drools business rules engine in the DRL format. The main focus is the transformation of GUHA association rules to DRL.

Action rules describe possible transitions of objects from one state to another with respect to a distinguished attribute. Early research on action rule discovery usually required the extraction of classification rules before constructing any action rule. Newest algorithms discover action rules directly from a decision system. To our knowledge, all these algorithms assume that all attributes ar...

Association Rule Mining algorithms operate on a data matrix (e.g., customers products) to derive association rules [2, 23]. We propose a new paradigm, namely, Ratio Rules, which are quanti able in that we can measure the \goodness" of a set of discovered rules. We propose to use the \guessing error" as a measure of the \goodness", that is, the rootmean-square error of the reconstructed values o...

This paper presents the implementation of a classification system based on learning of association rules in conjunction with Drools rule engine. The rules are interactively discovered with a web-based data mining system EasyMiner.eu. The rules are approved and edited by the domain expert before they are deployed for classification.

Many business organizations generate a huge amount of transaction data. Association rule mining is a powerful analysis tool to extract the useful meanings and associations from large databases and many automated systems have been developed for mining association rules. However, most of these systems usually mine many association rules from large databases and it is not easy for a user to extrac...