Mining frequent subtrees from databases of labeled trees is a new research field that has
many practical applications in areas such as computer networks, Web mining …

Graph mining is an important research area within the domain of data mining. The field of
study concentrates on the identification of frequent subgraphs within graph data sets. The …

This paper is focused on modern approaches to machine learning, most of which are as yet
used infrequently or not at all in chemoinformatics. Machine learning methods are …

Abstract Machine Learning (ML) and Data Mining (DM) build tools intended to help users
solve data‐related problems that are infeasible for “unaugmented” humans. Tools need …

Tree structures are used extensively in domains such as computational biology, pattern
recognition, XML databases, computer networks, and so on. One important problem in …

As business environments become more competitive, organizations strive to improve their
business processes to reduce costs and increase quality and productivity. As process …

Existing anti‐malware products usually use signature‐based techniques as their main
detection engine. Although these methods are very fast, they are unable to provide effective …

Tree structures are used extensively in domains such as computational biology, pattern
recognition, XML databases, computer networks, and so on. In this paper, we first present …

Most previously proposed frequent graph mining algorithms are intended to find the
complete set of all frequent, closed subgraphs. However, in many cases only a subset of the …

In this paper we present a novel algorithm for discovering tree patterns in a tree database.
This algorithm uses a relaxed tree inclusion definition, making the problem more complex …