Redescription Mining (eBook)

Esther Galbrun, Pauli Miettinen (Autoren)

eBook Download: PDF

2018 | 1st ed. 2017
XI, 80 Seiten
Springer International Publishing (Verlag)
978-3-319-72889-6 (ISBN)

Infos

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. 

Esther Galbrun is a junior research scientist at Inria Nancy--Grand Est, France. She was previously a postdoctoral researcher at the CS department of Boston University, USA, after having obtained her PhD in 2014 from the CS department at the University of Helsinki, Finland, on the topic of redescription mining.

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.

Inhalt

Preface 6
Contents 7
List of Figures 9
List of Symbols 10
1 What Is Redescription Mining 11
1.1 First Examples of Redescriptions 11
1.2 Formal Definitions 15
1.2.1 The Data 15
1.2.2 The Descriptions 16
1.2.3 The Redescriptions 18
1.2.4 Other Constraints 21
1.2.5 Distance Functions: Why Jaccard? 23
1.2.6 Sets of Redescriptions 26
1.3 Related Data Mining Problems 28
1.4 A Short History 30
References 31
2 Algorithms for Redescription Mining 34
2.1 Finding Queries Using Itemset Mining 35
2.1.1 The MID Algorithm 37
2.1.2 Mining Redescriptions with the CHARM-L Algorithm 38
2.2 Queries Based on Decision Trees and Forests 39
2.2.1 The CARTwheels Algorithm 41
2.2.2 The SplitT and LayeredT Algorithms 44
2.2.3 The CLUS-RM Algorithm 47
2.3 Growing the Queries Greedily 49
2.3.1 The ReReMi Algorithm 49
2.4 A Comparative Discussion 53
2.5 Handling Missing Values 55
References 57
3 Applications, Variants, and Extensions of Redescription Mining 59
3.1 Applications of Redescription Mining 59
3.1.1 In Biology 60
3.1.2 In Ecology 63
3.1.3 In Social and Political Sciences and in Economics 64
3.1.4 In Engineering 67
3.2 Relational Redescription Mining 69
3.2.1 An Example of Relational Redescriptions 69
3.2.2 Formal Definition 71
3.3 Storytelling 74
3.3.1 Definition and Algorithms 75
3.3.2 Applications 77
3.4 Future Work: Richer Query Languages 81
3.4.1 Time-Series Redescriptions 81
3.4.2 Subgraph Redescriptions 83
3.4.3 Multi-Query and Multimodal Redescriptions 84
References 87

Produkt-Details

• Erscheint lt. Verlag: 10.1.2018
• Reihe/Serie: SpringerBriefs in Computer Science
• Reihe/Serie: SpringerBriefs in Computer Science
• Zusatzinfo: XI, 80 p. 18 illus., 14 illus. in color.
• Verlagsort: Cham
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik
• Schlagworte: Alternative characterizations • Data Mining • Interpretable patterns • Multi-view data analysis • Redescription mining • visualizations
• ISBN-10: 3-319-72889-X / 331972889X
• ISBN-13: 978-3-319-72889-6 / 9783319728896