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 …

General purpose computation using Graphic Processing Units (GPUs) is a well‐established
research area focusing on high‐performance computing solutions for massively …

Pattern mining is a fundamental technique of data mining to discover interesting correlations
in the data set. There are several variations of pattern mining, such as frequent itemset …

This book is intended to provide a general and comprehensible overview of the field of
pattern mining with evolutionary algorithms. To do so, the book provides formal definitions …

Frequent itemset mining is widely used as a fundamental data mining technique. However,
as the data size increases, the relatively slow performances of the existing methods hinder …

Association rule mining is a well-known data mining task, but it requires much computational
time and memory when mining large scale data sets of high dimensionality. This is mainly …

Frequent itemset mining is a fundamental problem in data mining area because frequent
itemsets have been extensively used in reasoning, classifying, clustering, and so on. To …

Frequent item set mining (FIM) algorithms extract subsets of items that occurs frequently in a
collection of sets. FIM is a key analysis in several data mining applications, and the FIM tools …

Vertical data structure is very important for closed frequent itemset mining. All closed
frequent itemsets can be found by simply using the operations of AND/OR. However, it …

Graph data mining has been a crucial as well as inevitable area of research. Large amounts
of graph data are produced in many areas, such as Bioinformatics, Cheminformatics, Social …