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 …
T You, Y Sun, Y Zhang, J Chen, P Zhang… - Expert Systems with …, 2022 - Elsevier
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 …
Y Tong, L Chen, Y Cheng, PS Yu - arXiv preprint arXiv:1208.0292, 2012 - arxiv.org
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 …