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An Evaluation of Clustering and Classification Algorithms in Life-Logging Devices
Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.
2015 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Using life-logging devices and wearables is a growing trend in today’s society. These yield vast amounts of information, data that is not directly overseeable or graspable at a glance due to its size. Gathering a qualitative, comprehensible overview over this quantitative information is essential for life-logging services to serve its purpose.

This thesis provides an overview comparison of CLARANS, DBSCAN and SLINK, representing different branches of clustering algorithm types, as tools for activity detection in geo-spatial data sets. These activities are then classified using a simple model with model parameters learned via Bayesian inference, as a demonstration of a different branch of clustering.

Results are provided using Silhouettes as evaluation for geo-spatial clustering and a user study for the end classification. The results are promising as an outline for a framework of classification and activity detection, and shed lights on various pitfalls that might be encountered during implementation of such service.

Place, publisher, year, edition, pages
2015. , 74 p.
Keyword [en]
Clustering, Classification, DBSCAN, CLARANS, SLINK, Life-Logging, Bayesian Inference
National Category
Computer Systems
URN: urn:nbn:se:liu:diva-121630ISRN: LIU-IDA/LITH-EX-A–15/024—SEOAI: diva2:857381
External cooperation
Narrative AB
Subject / course
Computer Engineering
2015-06-10, Charles Babbage, 07:54 (English)
Available from: 2015-10-01 Created: 2015-09-29 Last updated: 2015-10-01Bibliographically approved

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