Modeling and clustering users with evolving profiles in usage streams

Handle URI:
http://hdl.handle.net/10754/575807
Title:
Modeling and clustering users with evolving profiles in usage streams
Authors:
Zhang, Chongsheng; Masseglia, Florent; Zhang, Xiangliang ( 0000-0002-3574-5665 )
Abstract:
Today, there is an increasing need of data stream mining technology to discover important patterns on the fly. Existing data stream models and algorithms commonly assume that users' records or profiles in data streams will not be updated or revised once they arrive. Nevertheless, in various applications such asWeb usage, the records/profiles of the users can evolve along time. This kind of streaming data evolves in two forms, the streaming of tuples or transactions as in the case of traditional data streams, and more importantly, the evolving of user records/profiles inside the streams. Such data streams bring difficulties on modeling and clustering for exploring users' behaviors. In this paper, we propose three models to summarize this kind of data streams, which are the batch model, the Evolving Objects (EO) model and the Dynamic Data Stream (DDS) model. Through creating, updating and deleting user profiles, these models summarize the behaviors of each user as a profile object. Based upon these models, clustering algorithms are employed to discover interesting user groups from the profile objects. We have evaluated all the proposed models on a large real-world data set, showing that the DDS model summarizes the data streams with evolving tuples more efficiently and effectively, and provides better basis for clustering users than the other two models. © 2012 IEEE.
KAUST Department:
Machine Intelligence & kNowledge Engineering Lab; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
2012 19th International Symposium on Temporal Representation and Reasoning
Conference/Event name:
2012 19th International Symposium on Temporal Representation and Reasoning, TIME 2012
Issue Date:
Sep-2012
DOI:
10.1109/TIME.2012.16
Type:
Conference Paper
ISBN:
9780769548029
Appears in Collections:
Conference Papers; Computer Science Program; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorZhang, Chongshengen
dc.contributor.authorMasseglia, Florenten
dc.contributor.authorZhang, Xiangliangen
dc.date.accessioned2015-08-24T09:26:44Zen
dc.date.available2015-08-24T09:26:44Zen
dc.date.issued2012-09en
dc.identifier.isbn9780769548029en
dc.identifier.doi10.1109/TIME.2012.16en
dc.identifier.urihttp://hdl.handle.net/10754/575807en
dc.description.abstractToday, there is an increasing need of data stream mining technology to discover important patterns on the fly. Existing data stream models and algorithms commonly assume that users' records or profiles in data streams will not be updated or revised once they arrive. Nevertheless, in various applications such asWeb usage, the records/profiles of the users can evolve along time. This kind of streaming data evolves in two forms, the streaming of tuples or transactions as in the case of traditional data streams, and more importantly, the evolving of user records/profiles inside the streams. Such data streams bring difficulties on modeling and clustering for exploring users' behaviors. In this paper, we propose three models to summarize this kind of data streams, which are the batch model, the Evolving Objects (EO) model and the Dynamic Data Stream (DDS) model. Through creating, updating and deleting user profiles, these models summarize the behaviors of each user as a profile object. Based upon these models, clustering algorithms are employed to discover interesting user groups from the profile objects. We have evaluated all the proposed models on a large real-world data set, showing that the DDS model summarizes the data streams with evolving tuples more efficiently and effectively, and provides better basis for clustering users than the other two models. © 2012 IEEE.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.titleModeling and clustering users with evolving profiles in usage streamsen
dc.typeConference Paperen
dc.contributor.departmentMachine Intelligence & kNowledge Engineering Laben
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.identifier.journal2012 19th International Symposium on Temporal Representation and Reasoningen
dc.conference.date12 September 2012 through 14 September 2012en
dc.conference.name2012 19th International Symposium on Temporal Representation and Reasoning, TIME 2012en
dc.conference.locationLeicesteren
dc.contributor.institutionSchool of Computer and Information Engineering, Henan University, Chinaen
dc.contributor.institutionZenith Team, Inria, Franceen
kaust.authorZhang, Xiangliangen
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