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 …

The main purpose of data mining and analytics is to find novel, potentially useful patterns
that can be utilized in real-world applications to derive beneficial knowledge. For identifying …

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 …

It is widely known that there is a lot of useful information hidden in big data, leading to a new
saying that “data is money.” Thus, it is prevalent for individuals to mine crucial information for …

In recent years, high-utility itemset mining has emerged as an important data mining task.
However, it remains computationally expensive both in terms of runtime and memory …

High-utility sequential pattern (HUSP) mining is an emerging topic in the field of knowledge
discovery in databases. It consists of discovering subsequences that have a high utility …

N-list is a novel data structure proposed in recent years. It has been proven to be very
efficient for mining frequent itemsets. In this paper, we present PrePost+, a high-performance …

High utility pattern mining is an emerging data science task, which consists of discovering
patterns having a high importance in databases. The utility of a pattern can be measured in …

One-off sequential pattern mining (SPM)(or SPM under the one-off condition) is a kind of
repetitive SPM with gap constraints, and has been widely applied in many fields. However …

Utility is an important concept in Economics. A variety of applications consider utility in real-
life situations, which has lead to the emergence of utility-oriented mining (also called utility …