  Discovering patterns from data is an important task in data mining. There
exist techniques to find large collections of many kinds of patterns from data
very efficiently. A collection of patterns can be regarded as a summary of the
data. A major difficulty with patterns is that pattern collections summarizing
the data well are often very large.
  In this dissertation we describe methods for summarizing pattern collections
in order to make them also more understandable. More specifically, we focus on
the following themes: 1) Quality value simplifications. 2) Pattern orderings.
3) Pattern chains and antichains. 4) Change profiles. 5) Inverse pattern
discovery.
