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 …

Mining useful patterns from varied types of databases is an important research topic, which
has many reallife applications. Most studies have considered the frequency as sole …

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 …

Pattern mining is one of the most important tasks to extract meaningful and useful
information from raw data. This task aims to extract item-sets that represent any type of …

The problem of discovering high-utility itemsets (HUIs) in transaction databases, which is an
extension of Frequent Itemset Mining, is a commonly encountered mining task. Researchers …

Association rule mining is one of the most important tasks to describe raw data. Although
many efficient algorithms have been developed to this aim, existing algorithms do not work …

To understand current situation in specific scenarios, valuable knowledge should be mined
from both historical data and emerging new data. However, most existing algorithms take the …

Analyzing customer transactions to discover high-utility itemsets is a popular task, which
consists of finding the sets of items that are purchased together and yield a high profit …

This paper considers frequent itemsets mining in transactional databases. It introduces a
new accurate single scan approach for frequent itemset mining (SSFIM), a heuristic as an …

Multi-label learning has become an important area of research owing to the increasing
number of real-world problems that contain multi-label data. Data labeling is an expensive …