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

Temporal data mining deals with the harvesting of useful information from temporal data.
New initiatives in health care and business organizations have increased the importance of …

Based on the intuition that frequent patterns can be used to predict the next few items that
users would want to access, sequential pattern mining-based next-items recommendation …

Mining sequential patterns is an important research issue in data mining and knowledge
discovery with broad applications. However, the existing sequential pattern mining …

In this uncertain world, data uncertainty is inherent in many applications and its importance
is growing drastically due to the rapid development of modern technologies. Nowadays …

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) …

Frequent pattern mining extracts most frequent patterns from databases. These frequency-
based frameworks have limitations in representing users' interest in many cases. In business …

Capturing and understanding user interests are an important part of social media analytics.
Users of social media sites often belong to multiple interest communities, and their interests …

Sequential pattern mining, including weighted sequential pattern mining, has been attracting
much attention since it is one of the essential data mining tasks with broad applications. The …

In this paper, we propose an efficient graph-based mining (GBM) algorithm for mining the
frequent trajectory patterns in a spatial–temporal database. The proposed method …