Sequences of events, items, or tokens occurring in an ordered metric space appear often in
data and the requirement to detect and analyze frequent subsequences is a common …

Predictive data mining in clinical medicine deals with learning models to predict patients'
health. The models can be devoted to support clinicians in diagnostic, therapeutic, or …

This chapter will provide a detailed survey of frequent pattern mining algorithms. A wide
variety of algorithms will be covered starting from Apriori. Many algorithms such as Eclat …

From basic data mining concepts to state-of-the-art advances, this book covers the theory of
the subject as well as its application in a variety of fields. It discusses the incorporation of …

Improving the performance of classifiers using pattern mining techniques has been an active
topic of data mining research. In this work we introduce the recent temporal pattern mining …

Supplying a comprehensive overview of healthcare analytics research, Healthcare Data
Analytics provides an understanding of the analytical techniques currently available to solve …

We study the problem of learning classification models from complex multivariate temporal
data encountered in electronic health record systems. The challenge is to define a good set …

Existing temporal pattern mining assumes that events do not have any duration. However,
events in many real world applications have durations, and the relationships among these …

Biomedical data, in particular electronic medical records data, include a large number of
variables sampled in irregular fashion, often including both time point and time intervals …

Previous research on mining sequential patterns mainly focused on discovering patterns
from point-based event data. Little effort has been put toward mining patterns from interval …