Discovering unexpected and useful patterns in databases is a fundamental data mining task.
In recent years, a trend in data mining has been to design algorithms for discovering …

The age of big data is now coming. But the traditional data analytics may not be able to
handle such large quantities of data. The question that arises now is, how to develop a high …

In the last two decades, machine learning research and practice has focused on batch
learning, usually with small datasets. Nowadays there are applications in which the data are …

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 …

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 …

Sequential pattern mining, which finds the set of frequent subsequences in sequence
databases, is an important data-mining task and has broad applications. Usually, sequence …

More and more weakly structured, and irregular data sources are becoming available every
day. The schema of these sources is useful for a number of tasks, such as query answering …

Many real life sequence databases grow incrementally. It is undesirable to mine sequential
patterns from scratch each time when a small set of sequences grow, or when some new …

Data mining [Chen et al. 1996] is the process of extracting interesting (non-trivial, implicit,
previously unknown and potentially useful) information or patterns from large information …

Generic sequential pattern mining problem aims to mine the set of sequential patterns from a
sequential database that satisfies a minimum support or occurrence threshold constraint …