The main purpose of data mining and analytics is to find novel, potentially useful patterns
that can be utilized in real-world applications to derive beneficial knowledge. For identifying …

The task of finding correlations between items in a dataset, association mining, has received
considerable attention over the last decade. This article presents a survey of association …

This chapter will provide a detailed survey of frequent pattern mining algorithms. A wide
variety of algorithms will be covered starting from Apriori. Many algorithms such as Eclat …

Abstract Weak isosinglet Neutral Heavy Leptons (vm) have been searched for using data
collected by the DELPHI detector corresponding to 3.3× 10 6 hadronic Z 0 decays at LEP1 …

For a transaction database, a frequent itemset is an itemset included in at least a specified
number of transactions. A frequent itemset P is maximal if P is included in no other frequent …

With the advent of the Web along with the unprecedented amount of information available in
electronic format, conceptual data analysis is more useful and practical than ever, because …

Granular computing and knowledge reduction are two basic issues in knowledge
representation and data mining. Granular structure of concept lattices with application in …

We address the issues of discovering significant binary relationships in transaction datasets
in a weighted setting. Traditional model of association rule mining is adapted to handle …

We introduce the notion of iceberg concept lattices and show their use in knowledge
discovery in databases. Iceberg lattices are a conceptual clustering method, which is well …

Mining patterns with high utilization (or called high-utility itemset mining, HUIM) is
considered a major issue in recent decades especially in the market (eg, supermarket) …