An Introduction to Frequent Pattern Mining - Frequent Pattern Mining - page 14

techniques can also be used to determine outliers from Web log sequences [ 1 ]. Fre-
quent patterns are also used for trajectory classification and outlier analysis [ 49 - 48 ].
Frequent pattern mining methods can also be used in order to determine relevant
rules and patterns in spatial data, as they related to spatial and non-spatial properties
of objects. For example, an association rule could be created from the relationships
of land temperatures of “nearby” geographical locations. In the context of spatiotem-
poral data, the relationships between the motions of different objects could be used to
create spatiotemporal frequent patterns. Frequent pattern mining methods have been
used for finding patterns in biological and chemical data [ 42 , 29 , 75 ]. In addition,
because software programs can be represented as graphs, frequent pattern mining
methods can be used in order to find logical bugs from program execution traces
[ 51 ]. Numerous applications of frequent pattern mining are discussed in Chap. 18.

7 Conclusions and Summary

Frequent pattern mining is one of four major problems in the data mining domain.
This chapter provides an overview of the major topics in frequent pattern mining. The
earliest work in this area was focussed on determining the efficient algorithms for
frequent pattern mining, and variants such as long pattern mining, interesting pattern
mining, constraint-based pattern mining, and compression. In recent years scalability
has become an issue because of the massive amounts of data that continue to be
created in various applications. In addition, because of advances in data collection
technology, advanced data types such as temporal data, spatiotemporal data, graph
data, and uncertain data have become more common. Such data types have numerous
applications to other data mining problems such as clustering and classification. In
addition, such data types are used quite often in various temporal applications, such
as the Web log analytics.

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