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Temporally Stable Clusters of Movie Series: A Machine Learning Approach to Content Segmentation
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Clustering techniques have been shown to provide insight in various domains and applications. Adaptive evolutionary spectral clustering is a state-of-the-art method to obtain temporally stable clustering results from time-stamped data. This thesis explores the use of adaptive evolutionary spectral clustering to perform a clustering of film series into groups based on video streaming data. The developed method successfully performs a stable segmentation of film series into groups and introduces a number of extensions to the framework within the context of video on demand. We find that the implemented method allows for reasoning about clusters from an evolutionary perspective and that the state-of-the-art can be extended to introduce a dynamic number of clusters without negatively impacting the stability of properties of clusters.

Abstract [sv]

Klustringsalgoritmer har använts och implementerats inom ramen för en mängd olika sammanhang. Adaptive evolutionary spectral clustering är en klustringmetod som används för att uppnå kluster som är stabila över tid genom tidsstämplad data. Detta examensarbete implementerar och utforskar användning och påbyggnad av metoden för att segmentera filmserier i grupper genom video streaming data. Den utvecklade modellen segmenterar filmserier i stabila grupper och introducerar en mängd utvecklingar inom video on demand domänen. Vi finner att den implementerade metoden möjliggör klusteranalys från ett evolutionärt perspektiv och att metoden kan utvecklas genom att introducera ett dynamiskt antal kluster utan att negativt påverka stabiliteten eller egenskaperna hos kluster.

Place, publisher, year, edition, pages
2019. , p. 63
Series
TRITA-EECS-EX ; 2019:843
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-271213OAI: oai:DiVA.org:kth-271213DiVA, id: diva2:1416021
External cooperation
HBO Europe
Supervisors
Examiners
Available from: 2020-03-20 Created: 2020-03-20 Last updated: 2020-03-20Bibliographically approved

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2627282930313229 of 204
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