Frequent itemset mining (FIM) is an essential task within data analysis since it is responsible
for extracting frequently occurring events, patterns, or items in data. Insights from such …

Itemset mining is an important subfield of data mining, which consists of discovering
interesting and useful patterns in transaction databases. The traditional task of frequent …

This textbook explores the different aspects of data mining from the fundamentals to the
complex data types and their applications, capturing the wide diversity of problem domains …

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 …

It is widely known that there is a lot of useful information hidden in big data, leading to a new
saying that “data is money.” Thus, it is prevalent for individuals to mine crucial information for …

The set of frequent closed itemsets uniquely determines the exact frequency of all itemsets,
yet it can be orders of magnitude smaller than the set of all frequent itemsets. In this paper …

As data mining techniques are being increasingly applied to non-traditional domains,
existing approaches for finding frequent itemsets cannot be used as they cannot model the …

Graph has become increasingly important in modelling complicated structures and
schemaless data such as proteins, chemical compounds, and XML documents. Given a …

Efficient algorithms for mining frequent itemsets are crucial for mining association rules as
well as for many other data mining tasks. Methods for mining frequent itemsets have been …

Frequent item set mining is one of the best known and most popular data mining methods.
Originally developed for market basket analysis, it is used nowadays for almost any task that …