In recent years, advanced machine learning and artificial intelligence techniques have
gained popularity due to their ability to solve problems across various domains with high …

The set of all frequent patterns that are extracted from a single network can be huge. A
technique recently proposed for obtaining a compact, informative and useful set of patterns …

Mining frequent tree patterns has many applications in different areas such as XML data,
bioinformatics and World Wide Web. The crucial step in frequent pattern mining is frequency …

Frequent pattern mining is an important data mining task with a broad range of applications.
Initially focused on the discovery of frequent itemsets, studies were extended to mine …

A key task in mining tree-structured data is finding frequent embedded tree patterns, which
has two settings: the transactional setting and the per-occurrence setting. In the transactional …

Mining frequent patterns in a single network (graph) poses a number of challenges. Already
only to match one path pattern to a network under subgraph isomorphism is NP-complete …

A well-studied problem in data mining is large itemset mining. To address this problem over
very large datasets, several approximate algorithms have been introduced, where an …

Extracting patterns from web usage data helps to facilitate better web personalization and
web structure readjustment. The classical frequency-based sequence mining techniques …

Extracting frequent subtrees from the tree structured data has important applications in Web
mining. In this paper, we introduce a novel canonical form for rooted labelled unordered …

Frequent pattern mining is an important data mining task with a broad range of applications.
Initially focused on the discovery of frequent itemsets, studies were extended to mine …