Association Discovery in Two-View Data

Two-view datasets are datasets whose attributes are naturally split into two sets, each providing a different view on the same set of objects. We introduce the task of finding small and non-redundant sets of associations that describe how the two views are related. To achieve this, we propose a novel approach in which sets of rules are used to translate one view to the other and vice versa. Our models, dubbed translation tables, contain both unidirectional and bidirectional rules that span both views and provide lossless translation from either of the views to the opposite view. To be able to evaluate different translation tables and perform model selection, we present a score based on the Minimum Description Length (MDL) principle. Next, we introduce three TRANSLATOR algorithms to find good models according to this score. The first algorithm is parameter-free and iteratively adds the rule that improves compression most. The other two algorithms use heuristics to achieve better trade-offs between runtime and compression. The empirical evaluation on real-world data demonstrates that only modest numbers of associations are needed to characterize the two-view structure present in the data, while the obtained translation rules are easily interpretable and provide insight into the data.

[1]  Jorma Rissanen,et al.  Minimum Description Length Principle , 2010, Encyclopedia of Machine Learning.

[2]  Lior Rokach,et al.  Data Mining And Knowledge Discovery Handbook , 2005 .

[3]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[4]  Naren Ramakrishnan,et al.  Redescription Mining: Structure Theory and Algorithms , 2005, AAAI.

[5]  Pauli Miettinen,et al.  From black and white to full color: extending redescription mining outside the Boolean world , 2012, Stat. Anal. Data Min..

[6]  Christos Faloutsos,et al.  On data mining, compression, and Kolmogorov complexity , 2007, Data Mining and Knowledge Discovery.

[7]  Jilles Vreeken,et al.  Krimp: mining itemsets that compress , 2011, Data Mining and Knowledge Discovery.

[8]  Jilles Vreeken,et al.  The long and the short of it: summarising event sequences with serial episodes , 2012, KDD.

[9]  P. Grünwald.  The Minimum Description Length Principle (Adaptive Computation and Machine Learning) , 2007 .

[10]  Jilles Vreeken,et al.  Item Sets that Compress , 2006, SDM.

[11]  Tijl De Bie,et al.  A Theoretical Framework for Exploratory Data Mining: Recent Insights and Challenges Ahead , 2013, ECML/PKDD.

[12]  Jilles Vreeken,et al.  Identifying the components , 2009, Data Mining and Knowledge Discovery.

[13]  Hans-Peter Kriegel,et al.  Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering , 2009, TKDD.

[14]  Luc De Raedt,et al.  Constraint-Based Pattern Set Mining , 2007, SDM.

[15]  Wouter Duivesteijn,et al.  Exceptional Model Mining , 2008, Data Mining and Knowledge Discovery.

[16]  Arthur Zimek,et al.  When Pattern Met Subspace Cluster , 2011, MultiClust@ECML/PKDD.

[17]  Edward Omiecinski,et al.  Alternative Interest Measures for Mining Associations in Databases , 2003, IEEE Trans. Knowl. Data Eng..

[18]  Geoffrey I. Webb.  Discovering Significant Patterns , 2007, Machine Learning.

[19]  Srinivasan Parthasarathy,et al.  New Algorithms for Fast Discovery of Association Rules , 1997, KDD.

[20]  Steffen Bickel,et al.  Multi-view clustering , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

[21]  Grigorios Tsoumakas,et al.  Mining Multi-label Data , 2010, Data Mining and Knowledge Discovery Handbook.

[22]  Mohammed J. Zaki.  Mining Non-Redundant Association Rules , 2004, Data Min. Knowl. Discov..

[23]  Michael R. Berthold,et al.  Learning in parallel universes , 2010, Data Mining and Knowledge Discovery.

[24]  Philip S. Yu,et al.  Mining top-K high utility itemsets , 2012, KDD.

[25]  Jan Zima,et al.  The Atlas of European Mammals , 1999 .