Association rules are a key data-mining tool and as such have been well researched. So far, this research has focused predominantly on databases containing categorical data only. However, many real-world databases contain quantitative attributes and current solutions for this case are so far inadequate. We introduce a new definition of quantitative association rules based on statistical inference theory. Our definition reflects the intuition that the goal of association rules is to find extraordinary and therefore interesting phenomena in databases. We also introduce the concept of sub-rules which can be applied to any type of association rule. Rigorous experimental evaluation on real-world datasets is presented, demonstrating the usefulness and characteristics of rules mined according to our definition. 1 Introduction Association Rules. The goal of data mining is to extract higher level information from an abundance of raw data. Association rules are a key tool used for this...

Abstract—The theory of fuzzy sets has been recognized as a suitable tool to model several kinds of patterns that can hold in data. In this paper, we are concerned with the development of a general model to discover association rules among items in a (crisp) set of fuzzy transactions. This general model can be particularized in several ways; each particular instance corresponds to a certain kind of pattern and/or repository of data. We describe some applications of this scheme, paying special attention to the discovery of fuzzy association rules in relational databases. Index Terms—Association rules, data mining, fuzzy transactions, quantified sentences. I.

The goal of data mining is to discover knowledge and reveal new, interesting and previously unknown information to the user. A central data mining tool is association rules. For events X and Y, an association rule is a rule of the type X Þ Y, with a certain probability. Classical use of association rules is with market-basket data resulting in rules such as "70% of people who buy beer also buy diapers". Association rules discover patterns and correlations that may be buried deep inside a database. They have therefore become a key data-mining tool and as such have been well researched. This research has focused mainly on the case of databases containing only categorical attributes. However, most real-world databases contain many quantitative attributes and current solutions for this case are so far inadequate. A satisfactory solution would be of great benefit to many fields, an example of one being medical research. We introduce a new definition of quantitative association rules based ...

Abstract. Quantitative Association Rule (QAR) mining has been rec-ognized an influential research problem over the last decade due to the popularity of quantitative databases and the usefulness of associ-ation rules in real life. Unlike Boolean Association Rules (BARs), which only consider boolean attributes, QARs consist of quantitative attributes which contain much richer information than the boolean attributes. How-ever, the combination of these quantitative attributes and their value in-tervals always gives rise to the generation of an explosively large number of itemsets, thereby severely degrading the mining efficiency. In this paper, we propose an information-theoretic approach to avoid un-rewarding combinations of both the attributes and their value intervals being generated in the mining process. We study the mutual information between the attributes in a quantitative database and devise a normal-ization on the mutual information to make it applicable in the context of QAR mining. To indicate the strong informative relationships among the

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