Motivated by the vast applications of knowledge graph and the increasing demand in
education domain, we propose a system, called KnowEdu, to automatically construct …

In data mining, Frequent Itemsets Mining is a technique used in several domains with
notable results. However, the large volume of data in modern datasets increases the …

Data uncertainty has been taken into a consideration for mining and discovery of its hidden
knowledge in a variety of applications. Due to the fact that the nature of closed sequences is …

In recent years, due to the wide applications of uncertain data, mining frequent itemsets over
uncertain databases has attracted much attention. In uncertain databases, the support of an …

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 …

Frequent pattern mining has become very useful and interesting to researchers due to its
high applicability. Different real-life databases (eg, sensor network, medical diagnosis data) …

We study the problem of clustering probabilistic graphs. Similar to the problem of clustering
standard graphs, probabilistic graph clustering has numerous applications, such as finding …

The data handled in emerging applications like location-based services, sensor monitoring
systems, and data integration, are often inexact in nature. In this paper, we study the …

Although data has become an important kind of commercial goods, there are few
appropriate online platforms to facilitate the trading of mobile crowd-sensed data so far. In …

We propose a model for clustering data with spatiotemporal intervals. This model is used to
effectively evaluate clusters of spatiotemporal interval data. A new energy function is used to …