Case Study of Data Mining Mutual Engagement

Abstract

This paper presents a case study of using conventional Data Mining techniques to identify clusters of creative contributions made by users in a collaborative music making system. Results of the clustering suggest that when people mutually engage with each other they tend to converge on similar creative contributions, whereas smaller clusters may indicate higher quality contributions. We also show how we used Data Mining to discriminate between kinds of creative contributions including: complex melodic structures, simple melodic structures, musical motifs, and rhythmical contributions. Our use of Data Mining does not require direct interaction with users and so it may be useful in real-world study contexts.

Contributors

Nick Bryan-Kinns

Conference

Publication date: July 2018

Publication date (Print): July 2018

Pages: 1-6

Affiliations

Queen Mary University of London

London, E1 4NS. UK

DOI: 10.14236/ewic/HCI2018.98

Copyright statement: © Bryan-Kinns. Published by BCS Learning and Development Ltd. Proceedings of British HCI 2018. Belfast, UK.

License:

This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Conference name: Proceedings of the 32nd International BCS Human Computer Interaction Conference

Conference acronym: HCI

Conference number: 32

Conference location: Belfast, UK

Conference date: 4 - 6 July 2018

Conference sponsor: Electronic Workshops in Computing (eWiC)

Conference theme: Human Computer Interaction Conference

Product

Product Information: 1477-9358 BCS Learning & Development

Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/HCI2018.98

Self URI (journal page): https://ewic.bcs.org/

Categories

Series title: Electronic Workshops in Computing