Given the recent explosion of interest in streaming data and online algorithms, clustering of
time-series subsequences, extracted via a sliding window, has received much attention. In …

Sequential rule mining is an important data mining task used in a wide range of applications.
However, current algorithms for discovering sequential rules common to several sequences …

Mining sequential rules from large databases is an important topic in data mining fields with
wide applications. Most of the relevant studies focused on finding sequential rules …

Spatiotemporal association pattern mining can discover interesting interdependent
relationships among various types of geospatial data. However, existing mining methods for …

Droughts are normal climate episodes, yet they are among the most expensive natural
disasters in the world. Knowledge about the timing, severity, and pattern of droughts on the …

Periodic pattern mining is a popular data mining task, which consists of identifying patterns
that periodically appear in data. Traditional periodic pattern mining algorithms are designed …

Sequential rule mining is an important data mining problem with multiple applications. An
important limitation of algorithms for mining sequential rules common to multiple sequences …

The problem of discovering frequent arrangements of temporal intervals is studied. It is
assumed that the database consists of sequences of events, where an event occurs during a …

This paper presents MOWCATL, an efficient method for mining frequent association rules
from multiple sequential data sets. Our goal is to find patterns in one or more sequences that …

Drought has an impact on many aspects of society. To help decision makers reduce the
impacts of drought, it is important to improve our understanding of the characteristics and …