Researchers have proposed weighted frequent itemset mining algorithms that reflect the
importance of items. The main focus of weighted frequent itemset mining concerns satisfying …

Most algorithms for frequent pattern mining use a support constraint to prune the
combinatorial search space but support-based pruning is not enough. After mining datasets …

In this paper, we revisit the frequent itemset mining (FIM) problem and focus on studying the
pattern growth approach. Existing pattern growth algorithms differ in several dimensions:(1) …

Given a sequence database and minimum support threshold, the task of sequential pattern
mining is to discover the complete set of sequential patterns in databases. From the …

Vertical mining methods are very effective for mining frequent patterns and usually
outperform horizontal mining methods. However, the vertical methods become ineffective …

Extensive efforts have been devoted to developing efficient algorithms for mining frequent
patterns. However, frequent pattern mining remains a time-consuming process, especially …

Frequent pattern mining is one of main concerns in data mining tasks. In frequent pattern
mining, closed frequent pattern mining and weighted frequent pattern mining are two main …

Real‐time data stream mining algorithms are largely based on binary datasets and do not
handle continuous quantitative data streams, especially in medical data mining field …

Frequent pattern mining has been extensively studied in the data mining field due to its
broad applications in mining association rules, correlations, closed frequent patterns, graph …

The objective of mining quantitative sequential patterns is to discover complete sets of
sequential patterns with quantities in databases. Although this novel type of pattern …