A Personality Based Design Approach Using Subgroup Discovery

Behrenbruch, Kay; Atzmüller, Martin; Evers, Christoph; Schmidt, Ludger; Stumme, Gerd; Geihs, Kurt

doi:10.1007/978-3-642-34347-6_16

Cited by 9 publications

(5 citation statements)

References 17 publications

(31 reference statements)

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“…An interdisciplinary review of applications of subgroup discovery is provided by . More recently, subgroup discovery was used, for example, to study the relationship between technology acceptance and personality by Behrenbruch et al (2012). Goh and Ang (2007) provide an accessible introduction to association rule mining aimed at behavioral researchers.…”

Section: Discovering Interesting Rulesmentioning

confidence: 99%

“…Selected recent applications include exploration of mathematics anxiety among engineering students (Herawan, Vitasari, & Abdullah, 2011) or discovering color–emotion relationships (Feng, Lesot, & Detyniecki, 2010). More recently, subgroup discovery was used to study relationships between technology acceptance and various personas presented by users (Behrenbruch et al, 2012). Goh and Ang (2007) provides an accessible introduction to association rule mining aimed at behavioral researchers.…”

Section: Tabular Datamentioning

confidence: 99%

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Advances in Machine Learning for the Behavioral Sciences

Kliegr

Bahník

Fürnkranz

2019

American Behavioral Scientist

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The areas of machine learning and knowledge discovery in databases have considerably matured in recent years. In this article, we briefly review recent developments as well as classical algorithms that stood the test of time. Our goal is to provide a general introduction into different tasks such as learning from tabular data, behavioral data, or textual data, with a particular focus on actual and potential applications in behavioral sciences. The supplemental appendix to the article also provides practical guidance for using the methods by pointing the reader to proven software implementations. The focus is on R, but we also cover some libraries in other programming languages as well as systems with easy-to-use graphical interfaces.

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Section: Discovering Interesting Rulesmentioning

confidence: 99%

Section: Tabular Datamentioning

confidence: 99%

Advances in Machine Learning for the Behavioral Sciences

Kliegr

Bahník

Fürnkranz

2019

American Behavioral Scientist

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“…Furthermore, for heterogenous data exceptional model mining (cf., Refs ) opens up a wide range of options. There are also applications in related fields, for example, in software engineering for requirements engineering and design.…”

Section: Tools and Applicationsmentioning

confidence: 99%

Subgroup discovery

Atzmueller

2015

WIREs Data Min & Knowl

185

102

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Subgroup discovery is a broadly applicable descriptive data mining technique for identifying interesting subgroups according to some property of interest. This article summarizes fundamentals of subgroup discovery, before that it also reviews algorithms and further advanced methodological issues. In addition, we briefly discuss tools and applications of subgroup discovery approaches. In that context, we also discuss experiences and lessons learned and outline some of the future directions in order to show the advantages and benefits of subgroup discovery.

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“…Then, efficient exhaustive methods are required due to the size of the large (exponential) pattern search space. Possible applications include the identification of characteristic patterns [11,13,23], analysis of node information in social interaction networks [12,81,107], or descriptive community mining approaches [17].…”

Section: Fast Exhaustive Exceptional Model Miningmentioning

confidence: 99%

Data Mining on Social Interaction Networks

Atzmueller¹

2014

Journal of Data Mining &Amp; Digital Humanities

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Social media and social networks have already woven themselves into the very fabric of everyday life. This results in a dramatic increase of social data capturing various relations between the users and their associated artifacts, both in online networks and the real world using ubiquitous devices. In this work, we consider social interaction networks from a data mining perspective - also with a special focus on real-world face-to-face contact networks: We combine data mining and social network analysis techniques for examining the networks in order to improve our understanding of the data, the modeled behavior, and its underlying emergent processes. Furthermore, we adapt, extend and apply known predictive data mining algorithms on social interaction networks. Additionally, we present novel methods for descriptive data mining for uncovering and extracting relations and patterns for hypothesis generation and exploration, in order to provide characteristic information about the data and networks. The presented approaches and methods aim at extracting valuable knowledge for enhancing the understanding of the respective data, and for supporting the users of the respective systems. We consider data from several social systems, like the social bookmarking system BibSonomy, the social resource sharing system flickr, and ubiquitous social systems: Specifically, we focus on data from the social conference guidance system Conferator and the social group interaction system MyGroup. This work first gives a short introduction into social interaction networks, before we describe several analysis results in the context of online social networks and real-world face-to-face contact networks. Next, we present predictive data mining methods, i.e., for localization, recommendation and link prediction. After that, we present novel descriptive data mining methods for mining communities and patterns.

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A Personality Based Design Approach Using Subgroup Discovery

Cited by 9 publications

References 17 publications

Advances in Machine Learning for the Behavioral Sciences

Advances in Machine Learning for the Behavioral Sciences

Subgroup discovery

Data Mining on Social Interaction Networks

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