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Cluster Analysis and Romantic Relationships
KTH, School of Electrical Engineering and Computer Science (EECS).
KTH, School of Electrical Engineering and Computer Science (EECS).
2018 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

In this article we apply k-means clustering to a previously unstudied dataset of mappings of personal values. As the data belongs to a dating service, we discuss whether clusters in the data could be used to construct features for a future classifier of romantic matches. We apply methods of feature reduction in an effort to improve results which we validate using silhouette scoring and the elbow method. Our results show little sign of there being any structures of interest in data.

To analyse the industry of online dating we conduct a traditional Porter analysis, coupled with a industry life-cycle analysis, to decide where the dating service – Valuesmatch – would be placed on the market. Then we make a comparative study of other actors in the industry to see what revenue models are prominent, to develop the value proposition of Valuesmatch. This work shows that the online dating market is segmented, and that customers interested in finding the one are more willing to pay premium for such a service.

We conclude by discussing alternative data mining approaches for further knowledge discovery in the data and how a revenue model could be structured given the strategic goals and ideological vision of the Valuesmatch team.

Place, publisher, year, edition, pages
2018.
Series
TRITA-EECS-EX ; 2018:432
Keywords [en]
K-means, clustering, online dating, life-cycle analysis
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-251380OAI: oai:DiVA.org:kth-251380DiVA, id: diva2:1315245
External cooperation
Valuesmatch
Supervisors
Examiners
Available from: 2019-05-13 Created: 2019-05-13 Last updated: 2019-05-13Bibliographically approved

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  • apa
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  • Other locale
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