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Information about the tutorial on redescription mining at the SIAM International Conference on Data Mining (SDM 2017), taking place in Houston, TX, USA, on Thursday April 27, 2017.

The tutorial slides can be found here.


A biologist interested in bioclimatic habitats of species needs to find geographical areas that admit two characterizations, one in terms of their climatic profile and one in terms of the occupying species. For a political analyst, matching the personal profiles of an election’s candidates to their viewpoints on various relevant issues might help cast light on the political scene and provide insight into the different candidates’ positions.

These are just two examples of a general problem setting where we need to identify correspondences between data that have different nature (species vs. climate, personal profiles vs. political viewpoints). Beyond these two examples, redescription mining has diverse practical applications, from detecting criminal networks to optimizing circuit designs.

To identify the correspondences over binary data sets, Ramakrishnan et al. proposed redescription mining in 2004. Subsequent research has extended the problem formulation to more complex correspondences and data types, making it applicable to wide variety of data analysis tasks.

In this tutorial we will give an overview of redescription mining, from the intuition behind the concept and its links to existing data analysis techniques to more recent developments in algorithms and interactive mining techniques. We will also cover five areas where applications for redescription mining have been proposed. The tutorial will give the attendants knowledge of the state-of-the-art techniques in redescription mining and open problems in method development, as well as examples and information on how to apply redescription mining to real-world data analysis problems.


Redescription mining, introduced in 2004 by Ramakrishnan et al. [Ram+04], aims at finding distinct common characterizations of the same objects. Like other multi-view analysis techniques, redescription mining is well suited to extract more coherent and relevant information, by exploiting different points of views on the same phenomena. This is an especially attractive feature as data grow increasingly diverse and heterogeneous. Recent advances in redescription mining algorithms and novel applications thereof — including the detection of criminal networks [Wu+14], the optimization circuit designs [Goe+10] and the analysis political opinions [GM16] —have increased the interest in this relatively nascent subfield of data analysis.

We will start by giving a conceptual overview of redescription mining, from the intuition behind the concept and its links to existing data analysis techniques, before presenting the more recent technical developments in algorithms and visual interactive mining methods. Then, we will cover five areas where practical applications of redescription mining have been proposed. These case studies are meant to illustrate the wide variety of potential applications of redescription mining, as well as to highlight the strengths and weaknesses of the current methods in the different applications (see the roadmap for more details).

From this tutorial, the data analysts will gain an overview of the redescription mining framework, a necessary understanding of the state-of-the-art methods, and a good grasp of the type of problems redescription mining can be applied to, respectively. The method developers, on the other hand, will learn the context and problem definition, the stepping stones for building their own tools, and the practical requirements of redescription mining algorithms through concrete examples, respectively. More generally, the attendees of the tutorial will hear about redescription mining and related methods and will hence be able to use these techniques in their own research.

Redescription mining is an important data analysis technique that has been covered previously only once in a tutorial. While still arguably not wide-spread, redescription-based methods currently attract increasing amounts of research interest (see, e.g. [GK14][GM14][ZGM15][Wu+14]). Furthermore, redescription mining techniques belong to the broader class of multi-view (or multi-modal) data analysis methods, which are becoming ever more important and relevant as the diversity and heterogeneity of available data grows.

Redescription mining employs machine learning techniques to automatically mine patterns from data, in order to support data exploration and knowledge discovery. Hence, it is particularly relevant to researchers from the data mining and analytics community who attend the SDM conference. This tutorial should help foster the research of redescription mining techniques and widen their use.


We expect the material to be interesting to a wide audience with diverse backgrounds, and to be accessible to anyone possessing basic knowledge of core data mining and machine learning techniques.

In this tutorial, you will learn what redescription mining is and what it is not. We will cover the two main views of redescription mining, association rule mining based and classification based, as well as the main related areas (subgroup discovery, subspace clustering, and multi-view data mining). You will learn about the state-of-the-art algorithms for finding redescriptions as well as interactive visualization techniques for exploring and interpreting them. The discussion will be illustrated with numerous examples throughout, and we will pay special attention to five practical applications of redescription mining in circuit design, bioinformatics, ecology, political sciences and data intelligence, respectively.

In short, you will learn about redescription mining and related methods and will hence be able to use these techniques in your own research.

This tutorial will last approximately 2 hours including. Questions will be welcomed throughout the tutorial and time will be left for discussion when wrapping up.


Tutors’ bios

Pauli Miettinen is a senior researcher and head of the area Data Mining at the Databases and Information Systems department of the Max-Planck Institute for Informatics, Germany. He is also an Adjunct Professor (docent) of computer science at the University of Helsinki, Finland, where he previously worked in Prof. Heikki Mannila’s group, and received his PhD in 2009. His main research interest is in Algorithmic Data Analysis. In particular, he has been working on matrix decompositions over non-standard algebras and their applications to data mining and on redescription mining. His research has resulted in numerous publications in top data mining venues, two best paper prices (PKDD ‘06 Best paper; PKDD ‘08 Best student paper), and an honorary mention at 2010 ACM SIGKDD Doctoral dissertation awards.

Pauli was the presenter of Decomposing Binary Matrices: Where Linear Algebra Meets Combinatorial Data Mining, a tutorial at ECML-PKDD 2012, and has given a number of invited talks and lectures.

Esther Galbrun is a junior research scientist at INRIA Nancy–Grand Est, France. She was previously a postdoctoral researcher and part-time lecturer at the Computer Science department of Boston University, MA, USA after having obtained her PhD in 2014 from the Computer Science department at the University of Helsinki, Finland, on the topic of redescription mining.


  1. The Theory
    1. What is redescription mining?
      1. Introductory examples
      2. Definitions and problem formalization [Gal13]
      3. Sets of redescriptions [DB11], [KGM16]
      4. The short history of redescriptions [Ram+04][PR05][ZR05][Kum+08], [GMM08][GM12a][GK14][ZGM15][KGM16]
    2. Related work [Agg15][Agr+98][Coh+01][BS04][Gup+13][KK14][NLW09][KZ09][LFK08][JMR08][Ram+04][Ume+09][Wro97], [Mie12][ZGM15]

  2. The Techniques 1. Visualizing and interacting with redescriptions

    1. Visualizing objects with maps and projections [GM12b]
    2. Visualizing queries [HS12][Ins85], [GM14]
    3. Visualizing a set of redescriptions [MS16]
    1. Algorithms for redescription mining
      1. Mining redescription with decision trees [Bre+84][Qui86][Ram+04], [ZGM15]
      2. Combining frequent itemsets into redescriptions [AS94][GNDR13][HPY00][Zak+97][ZH05][ZR05][ZZR06], [GMM08]
      3. Building redescriptions greedily [GMM08][GM12a]
  3. The Practise
    1. Bioinformatics: Biological pathways elucidation [JMR08][Kum07][RZ09]
    2. Ecology: Bioclimatic niche finding [PD03][PAS06][SN09][Thu+09], [GM12a][ZGM15]
    3. Circuit Design: Sequential Equivalence Checking [Goe+10]
    4. Political sciences: Analyzing poll data [GM16]
    5. Data Intelligence: Storytelling [Hos+12][Kum+08][Wu+14]
  • Wrap-Up
    • Summary



The tutorial slides can be found here.



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