Due to the rapid growth of resource sharing, distributed systems are developed, which can
be used to utilize the computations. Data mining (DM) provides powerful techniques for …

High-utility itemset mining is a popular data mining problem that considers utility factors,
such as quantity and unit profit of items besides frequency measure from the transactional …

High utility itemset mining considers the importance of items such as profit and item
quantities in transactions. Recently, mining high utility itemsets has emerged as one of the …

Processing changeable data streams in real time is one of the most important issues in the
data mining field due to its broad applications such as retail market analysis, wireless sensor …

Formal concept analysis (FCA) is currently considered an important formalism for knowledge
representation, extraction and analysis with applications in different areas. A problem …

Frequent pattern mining extracts most frequent patterns from databases. These frequency-
based frameworks have limitations in representing users' interest in many cases. In business …

Weighted single large graphs are often used to simulate complex systems, and thus mining
frequent subgraphs in a weighted large graph is an important issue that has attracted the …

Frequent itemset mining is a fundamental element with respect to many data mining
problems directed at finding interesting patterns in data. Recently the PrePost algorithm, a …

Pattern mining is a data mining technique used for discovering significant patterns and has
been applied to various applications such as disease analysis in medical databases and …

Frequent pattern mining (FPM) is an important topic in data mining for discovering the
implicit but useful information. Many algorithms have been proposed for this task but most of …