The use of patterns in predictive models is a topic that has received a lot of attention in
recent years. Pattern mining can help to obtain models for structured domains, such as …

Many graph pattern mining algorithms have been designed to identify recurring structures in
graphs. The main drawback of these approaches is that they often extract too many patterns …

In this paper we study predictive pattern mining problems where the goal is to construct a
predictive model based on a subset of predictive patterns in the database. Our main …

In this chapter we describe the use of patterns in the analysis of supervised data. We survey
the different settings for finding patterns as well as sets of patterns. The pattern mining …

With applications in biology, the world-wide web, and several other areas, mining of graph-
structured objects has received significant interest recently. One of the major research …

In this paper we introduce graph-evolution rules, a novel type of frequency-based pattern
that describe the evolution of large networks over time, at a local level. Given a sequence of …

Pattern mining is a key subfield of data mining that aims at developing algorithms to discover
interesting patterns in databases. The discovered patterns can be used to help …

With ever-increasing amounts of graph data from disparate sources, there has been a strong
need for exploiting significant graph patterns with user-specified objective functions. Most …

In this chapter we describe how to successfully apply the MDL principle to pattern mining. In
particular, we discuss how pattern-based models can be designed and induced by means of …

Data mining can be abstractly defined as the process of extracting concise and interesting
models (or, patterns) from large amounts of data. Unfortunately, conventional mining …