Discovering unexpected and useful patterns in databases is a fundamental data mining task.
In recent years, a trend in data mining has been to design algorithms for discovering …

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

High utility itemset mining is a challenging task in frequent pattern mining, which has wide
applications. The state-of-the-art algorithm is HUI-Miner. It adopts a vertical representation …

Modern web clickstream data consists of long, high-dimensional sequences of multivariate
events, making it difficult to analyze. Following the overarching principle that the visual …

Malware pose a serious threat to the computers of individuals, enterprises and other
organizations. In the Windows operating system (OS), Application Programming Interface …

Event sequence data is common to a broad range of application domains, from security to
health care to scholarly communication. This form of data captures information about the …

High utility itemset mining is an emerging data mining task, which consists of discovering
highly profitable itemsets (called high utility itemsets) in very large transactional databases …

Data Mining in general is the act of extracting interesting patterns and discovering non-trivial
knowledge from a large amount of data. Medical data mining can be used to understand the …

The research on massive open online courses (MOOCs) data analytics has mushroomed
recently because of the rapid development of MOOCs. The MOOC data not only contains …

Sequential Pattern Mining (SPM) is a branch of data mining that deals with finding
statistically relevant regularities of patterns in sequentially ordered data. It has been an …