High-utility itemset mining (HUIM) is a useful set of techniques for discovering patterns in
transaction databases, which considers both quantity and profit of items. However, most …

Data mining consists of deriving implicit, potentially meaningful and useful knowledge from
databases such as information about the most profitable items. High-utility itemset mining …

When focusing on the general area of data mining, high-utility itemset mining (HUIM) can be
defined as an offset of frequent itemset mining (FIM). It is known to emphasize more factors …

This paper considers the problem of mining probabilistic frequent itemsets in the sliding
window of an uncertain data stream. We design an effective in-memory index named PFIT to …

Uncertain data mining has attracted so much interest in many emerging applications over
the past decade. An issue of particular interest is to discover the frequent itemsets in …

Tracking frequent items (also called heavy hitters) is one of the most fundamental queries in
real-time data due to its wide applications, such as logistics monitoring, association rule …

In the Internet of Things (IoT) era, information is collected by sensor devices, resulting in
data loss or uncertain data and other consequences. We need to represent the uncertain …

Co-location pattern mining is an important task in spatial data mining. However, the
traditional framework of co-location pattern mining produces an exponential number of …

Pattern mining has been widely used to uncover interesting patterns from data. However,
one of its main problems is that it produces too many patterns and many of them are …

In the age of the Internet of Things (IoT), information is collected from sensor devices, which
can lead to data loss or data in doubt, among other things. We need to use probability …