Redescription Mining
Description
This book provides a gentle introduction to redescription mining, a versatile data mining tool that is useful to find distinct common characterizations of the same objects and, vice versa, to identify sets of objects that admit multiple shared descriptions. It is intended for readers who are familiar with basic data analysis techniques such as clustering, frequent itemset mining, and classification. Redescription mining is defined in a general way, making it applicable to different types of data. The general framework is made more concrete through many practical examples that show the versatility of redescription mining. The book also introduces the main algorithmic ideas for mining redescriptions, together with applications from various domains. The final part of the book contains variations and extensions of the basic redescription mining problem, and discusses some future directions and open questions.
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Persons
Pauli Miettinen is a senior researcher and head of the area Data Mining at the Max Planck Institute for Informatics, Germany. He is also an Adjunct Professor 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.
Content
2 - Contents [Seite 7]
3 - List of Figures [Seite 9]
4 - List of Symbols [Seite 10]
5 - 1 What Is Redescription Mining [Seite 11]
5.1 - 1.1 First Examples of Redescriptions [Seite 11]
5.2 - 1.2 Formal Definitions [Seite 15]
5.2.1 - 1.2.1 The Data [Seite 15]
5.2.2 - 1.2.2 The Descriptions [Seite 16]
5.2.3 - 1.2.3 The Redescriptions [Seite 18]
5.2.4 - 1.2.4 Other Constraints [Seite 21]
5.2.5 - 1.2.5 Distance Functions: Why Jaccard? [Seite 23]
5.2.6 - 1.2.6 Sets of Redescriptions [Seite 26]
5.3 - 1.3 Related Data Mining Problems [Seite 28]
5.4 - 1.4 A Short History [Seite 30]
5.5 - References [Seite 31]
6 - 2 Algorithms for Redescription Mining [Seite 34]
6.1 - 2.1 Finding Queries Using Itemset Mining [Seite 35]
6.1.1 - 2.1.1 The MID Algorithm [Seite 37]
6.1.2 - 2.1.2 Mining Redescriptions with the CHARM-L Algorithm [Seite 38]
6.2 - 2.2 Queries Based on Decision Trees and Forests [Seite 39]
6.2.1 - 2.2.1 The CARTwheels Algorithm [Seite 41]
6.2.2 - 2.2.2 The SplitT and LayeredT Algorithms [Seite 44]
6.2.3 - 2.2.3 The CLUS-RM Algorithm [Seite 47]
6.3 - 2.3 Growing the Queries Greedily [Seite 49]
6.3.1 - 2.3.1 The ReReMi Algorithm [Seite 49]
6.4 - 2.4 A Comparative Discussion [Seite 53]
6.5 - 2.5 Handling Missing Values [Seite 55]
6.6 - References [Seite 57]
7 - 3 Applications, Variants, and Extensions of Redescription Mining [Seite 59]
7.1 - 3.1 Applications of Redescription Mining [Seite 59]
7.1.1 - 3.1.1 In Biology [Seite 60]
7.1.2 - 3.1.2 In Ecology [Seite 63]
7.1.3 - 3.1.3 In Social and Political Sciences and in Economics [Seite 64]
7.1.4 - 3.1.4 In Engineering [Seite 67]
7.2 - 3.2 Relational Redescription Mining [Seite 69]
7.2.1 - 3.2.1 An Example of Relational Redescriptions [Seite 69]
7.2.2 - 3.2.2 Formal Definition [Seite 71]
7.3 - 3.3 Storytelling [Seite 74]
7.3.1 - 3.3.1 Definition and Algorithms [Seite 75]
7.3.2 - 3.3.2 Applications [Seite 77]
7.4 - 3.4 Future Work: Richer Query Languages [Seite 81]
7.4.1 - 3.4.1 Time-Series Redescriptions [Seite 81]
7.4.2 - 3.4.2 Subgraph Redescriptions [Seite 83]
7.4.3 - 3.4.3 Multi-Query and Multimodal Redescriptions [Seite 84]
7.5 - References [Seite 87]
