• Login
    View Item 
    •   Home
    • Research
    • Conference Papers
    • View Item
    •   Home
    • Research
    • Conference Papers
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of KAUSTCommunitiesIssue DateSubmit DateThis CollectionIssue DateSubmit Date

    My Account

    Login

    Quick Links

    Open Access PolicyORCID LibguidePlumX LibguideSubmit an Item

    Statistics

    Display statistics

    Modeling and clustering users with evolving profiles in usage streams

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Type
    Conference Paper
    Authors
    Zhang, Chongsheng
    Masseglia, Florent
    Zhang, Xiangliang cc
    KAUST Department
    Machine Intelligence & kNowledge Engineering Lab
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Date
    2012-09
    Permanent link to this record
    http://hdl.handle.net/10754/575807
    
    Metadata
    Show full item record
    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.
    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
    ISBN
    9780769548029
    DOI
    10.1109/TIME.2012.16
    ae974a485f413a2113503eed53cd6c53
    10.1109/TIME.2012.16
    Scopus Count
    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

    entitlement

     
    DSpace software copyright © 2002-2021  DuraSpace
    Quick Guide | Contact Us | Send Feedback
    Open Repository is a service hosted by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items. For anonymous users the allowed maximum amount is 50 search results.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.