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 …

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 …

Owing to important applications such as mining web page traversal sequences, many
algorithms have been introduced in the area of sequential pattern mining over the last …

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 …

We present cSPADE, an efficient algorithm for mining frequent sequences considering a
variety of syntactic constraints. These take the form of length or width limitations on the …

There exist emerging applications of data streams that require association rule mining, such
as network traffic monitoring and web click streams analysis. Different from data in traditional …

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 …

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 …

In this paper, we consider the problem of the incremental mining of sequential patterns when
new transactions or new customers are added to an original database. We present a 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 …