Database Reference
In-Depth Information
Chapter 16
Frequent Pattern Mining Algorithms
for Data Clustering
Arthur Zimek, Ira Assent and Jilles Vreeken
Abstract
Discovering clusters in subspaces, or subspace clustering and related clus-
tering paradigms, is a research field where we find many frequent pattern mining
related influences. In fact, as the first algorithms for subspace clustering were based
on frequent pattern mining algorithms, it is fair to say that frequent pattern mining was
at the cradle of subspace clustering—yet, it quickly developed into an independent
research field.
In this chapter, we discuss how frequent pattern mining algorithms have been
extended and generalized towards the discovery of local clusters in high-dimensional
data. In particular, we discuss several example algorithms for subspace clustering or
projected clustering as well as point out recent research questions and open topics in
this area relevant to researchers in either clustering or pattern mining.
Keywords
Subspace clustering
·
Monotonicity
·
Redundancy
1
Introduction
Data clustering is the task of discovering groups of objects in a data set that exhibit
high similarity. Clustering is an unsupervised task, in that we do not have access to
any additional information besides some geometry of the data, usually represented by
some distance function. Useful groups should consist of objects that are more similar
to each other than to objects assigned to other groups. The goal of the clustering results
is that it provides information for the user regarding different categories of objects
that the data set contains.
