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Modeling Evolving User Behavior via Sequential Clustering
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0001-7199-8080
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we address the problem of modeling the evolution of clusters over time by applying sequential clustering. We propose a sequential partitioning algorithm that can be applied for grouping distinct snapshots of streaming data so that a clustering model is built on each data snapshot. The algorithm is initialized by a clustering solution built on available historical data. Then a new clustering solution is generated on each data snapshot by applying a partitioning algorithm seeded with the centroids of the clustering model obtained at the previous time interval. At each step the algorithm also conducts model adapting operations in order to reflect the evolution in the clustering structure. In that way, it enables to deal with both incremental and dynamic aspects of modeling evolving behavior problems. In addition, the proposed approach is able to trace back evolution through the detection of clusters' transitions, such as splits and merges. We have illustrated and initially evaluated our ideas on household electricity consumption data. The results have shown that the proposed sequential clustering algorithm is robust to modeling evolving behavior by being enable to mine changes and update the model, respectively.

Place, publisher, year, edition, pages
Springer, 2019.
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-18666OAI: oai:DiVA.org:bth-18666DiVA, id: diva2:1352327
Conference
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECMLPKDD, Würzburg,16th to the 20th of September
Note

In progress of being published in Springer Lecture Notes.

Available from: 2019-09-18 Created: 2019-09-18 Last updated: 2019-11-18Bibliographically approved
In thesis
1. Data-Driven Techniques for Modeling and Analysis of User Behavior
Open this publication in new window or tab >>Data-Driven Techniques for Modeling and Analysis of User Behavior
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Our society is becoming more digitalized for each day. Now, we are able to gather data from individual users with higher resolution than ever. With the increased amount of data on an individual user level, we can analyze their behavior. This is of interest in many different domains, for example service providers wanting to improve their service for their customers. If they know how their service is used, they have more insight in how they can improve. But, it also imposes additional difficulties. When we reach the individual user, the irregularities in the regular behavior makes it harder to model the normal behavior.

In this thesis, we explore data-driven techniques to model and analyze user behaviors. We aim to evaluate existing as well as develop novel technologies to identify approaches that are suitable for use on an individual user level. We use both supervised and unsupervised learning methods to model the user behavior and evaluate the approaches on real world electricity consumption data.

Firstly, we analyze household electricity consumption data and investigate the use of regression to model the household's behavior. We identify consumption trends, how data granularity affects modeling, and we show that regression is a viable approach to model user behavior. Secondly, we use clustering analysis to profile individual households in terms of their electricity consumption. We compare two dissimilarity measures, how they affect the clustering analysis, and we investigate how the produced clustering solutions differ. Thirdly, we propose a sequential clustering algorithm to model evolving user behavior. We evaluate the proposed algorithm on electricity consumption data and show how the produced model can be used to identify and trace changes in the user's behavior. The algorithm is robust to evolving behaviors and handles both dynamic and incremental aspects of streaming data.

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2019
Series
Blekinge Institute of Technology Licentiate Dissertation Series, ISSN 1650-2140 ; 15
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-18667 (URN)978-91-7295-391-8 (ISBN)
Supervisors
Available from: 2019-11-15 Created: 2019-09-18 Last updated: 2019-11-21Bibliographically approved

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