Association rules are one of the most researched areas of data mining and have recently received much attention from the database community. They have proven to be quite useful in the marketing and retail communities as well as other more diverse fields. In this paper we provide an overview of association rule research.

MusicBrainz is a publicly available relational database that stores information about artists, releases, tracks and the relationship among them. We present the results of mining association rules from this dataset, with the aim of obtaining knowledge about artists and their work. We are able to obtain associations between features, such as native language, and quantify how likely it is for an a...

In this paper we describe a formal framework for the problem of mining association rules. The theoretical foundation is based on the field of formal concept analysis. A concept is composed of closed subsets of attributes (itemsets) and objects (transactions). We show that all frequent itemsets are uniquely determined by the frequent concepts. We further show how this lattice-theoretic framework...

Association rule mining typically results in large amounts of redundant rules. We introduce efficient methods for deriving tight bounds for confidences of association rules, given their subrules. If the lower and upper bounds of a rule coincide, the confidence is uniquely determined by the subrules and the rule can be pruned as redundant, or derivable, without any loss of information. Experimen...

Mining association rules is a fundamental data mining task. However, depending on the choice of the parameters (the minimum confidence and minimum support), current algorithms can become very slow and generate an extremely large amount of results or generate too few results, omitting valuable information. This is a serious problem because in practice users have limited resources for analyzing t...

Association rules are ”if-then rules” with two measures which quantify the support and confidence of the rule for a given data set. Having their origin in market basked analysis, association rules are now one of the most popular tools in data mining. This popularity is to a large part due to the availability of efficient algorithms following from the development of the Apriori algorithm. In the...

Many real-life problems require a partial classi cation of the data. We use the term \partial classi cation" to describe the discovery of models that show characteristics of the data classes, but may not cover all classes and all examples of any given class. Complete classi cation may be infeasible or undesirable when there are a very large number of class attributes, most attributes values are...

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Smart grid has been introduced to address power distribution system challenges. In conventional power distribution systems, when a power outage happens, the maintenance team tries to find the outage cause and mitigate it. After this, some information is documented in a dataset called the outage dataset. If the team can estimate the outage cause before searching for it, the restoration time will...

The importance of predicting Web users’ behaviour and their next movement has been recognised and discussed by many researchers lately. Association rules and Markov models are the most commonly used approaches for this type of prediction. Association rules tend to generate many rules, which result in contradictory predictions for a user session. Low order Markov models do not use enough user br...