Discovering unexpected and useful patterns in databases is a fundamental data mining task.
In recent years, a trend in data mining has been to design algorithms for discovering …

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 …

With the growing popularity of shared resources, large volumes of complex data of different
types are collected automatically. Traditional data mining algorithms generally have …

Mining high utility itemsets from a transactional database refers to the discovery of itemsets
with high utility like profits. Although a number of relevant algorithms have been proposed in …

Motivation: There is a strong demand in the genomic community to develop effective
algorithms to reliably identify genomic variants. Indel detection using next-gen data is …

Data Mining: Concepts and Techniques, Fourth Edition introduces concepts, principles, and
methods for mining patterns, knowledge, and models from various kinds of data for diverse …

Witten and Frank's textbook was one of two books that 1 used for a data mining class in the
Fall of 2001. The book covers all major methods of data mining that produce a knowledge …

Recently, the advancement of cognitive computing and three-way decisions has enabled in-
depth sequential pattern understanding through temporal association analysis. The main …

In recent years, high-utility itemset mining has emerged as an important data mining task.
However, it remains computationally expensive both in terms of runtime and memory …