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 …

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 …

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

To recommend the next item to a user in a transactional context is practical yet challenging
in applications such as marketing campaigns. Transactional context refers to the items that …

Online social and news media generate rich and timely information about real-world events
of all kinds. However, the huge amount of data available, along with the breadth of the user …

Frequent Itemset Mining (FIM) is one of the most well known techniques to extract
knowledge from data. The combinatorial explosion of FIM methods become even more …

Subgroup discovery is a broadly applicable descriptive data mining technique for identifying
interesting subgroups according to some property of interest. This article summarizes …

Recommender systems based on methods such as collaborative and content-based filtering
rely on extensive user profiles and item descriptors as well as on an extensive history of user …

Many parallelization techniques have been proposed to enhance the performance of the
Apriori-like frequent itemset mining algorithms. Characterized by both map and reduce …

Association rule mining (ARM) is a data mining technique to discover interesting
associations between datasets. The frequent pattern-growth (FP-growth) is an effective ARM …