article

On the classification of interactive user behaviour indices

Authors:

S. V. Raghavan and

R. KalyanakrishnanAuthors Info & Claims

ACM SIGMETRICS Performance Evaluation Review, Volume 13, Issue 2

Pages 40 - 48

https://doi.org/10.1145/317786.317809

Published: 01 August 1985 Publication History

Abstract

The concepts of user behaviour entropy and user behaviour mobility are proposed as indices for the description of user behaviour. The user behaviour indices are derivable from the mode probability vector and the mode transition matrix which adequately describe the behaviour dynamics of an interactive user. The user behaviour indices reduce the ((n*n)+n) dimensional parameter space to two dimensions only for classification, without loss of information related to the user behaviour dynamics. The classification of the users in an interactive educational environment using the user behaviour indices is presented as a case study.

Cited By

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Balajinath BRaghavan S(2001)Intrusion detection through learning behavior modelComputer Communications10.1016/S0140-3664(00)00364-924:12(1202-1212)Online publication date: 1-Jul-2001
https://dl.acm.org/doi/10.1016/S0140-3664%2800%2900364-9
Swaminathan NRaghavan S(2000)Intelligent prefetch in WWW using client behavior characterizationProceedings 8th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (Cat. No.PR00728)10.1109/MASCOT.2000.876424(13-19)Online publication date: 2000
https://doi.org/10.1109/MASCOT.2000.876424
Raghavan SVasukiammaiyar DHaring G(1995)Hierarchical approach to building generative networkload modelsComputer Networks and ISDN Systems10.1016/0169-7552(94)00012-I27:7(1193-1206)Online publication date: 1-May-1995
https://dl.acm.org/doi/10.1016/0169-7552%2894%2900012-I
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Index Terms

On the classification of interactive user behaviour indices
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms

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Information

Published In

cover image ACM SIGMETRICS Performance Evaluation Review

ACM SIGMETRICS Performance Evaluation Review Volume 13, Issue 2

Aug. 1985

205 pages

ISSN:0163-5999

DOI:10.1145/317786

Chairmen:
Herbert D. Schwetman
MCC
,
Jeffrey A. Brumfield
Univ. of Texas
,
Daniel A. Reed
Univ. of Illinois
,
Editor:
Blaine D. Gaither
New Mexico Institute of Technology, Socorro

Issue’s Table of Contents

SIGMETRICS '85: Proceedings of the 1985 ACM SIGMETRICS conference on Measurement and modeling of computer systems
August 1985
203 pages
ISBN:0897911695
DOI:10.1145/317795
Chairmen:
Herbert D. Schwetman
MCC, Austin, TX
,
Jeffrey A. Brumfield
Univ. of Texas
,
Daniel A. Reed
Univ. of Illinois
,
Editor:
Blaine D. Gaither
New Mexico Institute of Technology, Socorro

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 August 1985

Published in SIGMETRICS Volume 13, Issue 2

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Cited By

View all

Balajinath BRaghavan S(2001)Intrusion detection through learning behavior modelComputer Communications10.1016/S0140-3664(00)00364-924:12(1202-1212)Online publication date: 1-Jul-2001
https://dl.acm.org/doi/10.1016/S0140-3664%2800%2900364-9
Swaminathan NRaghavan S(2000)Intelligent prefetch in WWW using client behavior characterizationProceedings 8th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (Cat. No.PR00728)10.1109/MASCOT.2000.876424(13-19)Online publication date: 2000
https://doi.org/10.1109/MASCOT.2000.876424
Raghavan SVasukiammaiyar DHaring G(1995)Hierarchical approach to building generative networkload modelsComputer Networks and ISDN Systems10.1016/0169-7552(94)00012-I27:7(1193-1206)Online publication date: 1-May-1995
https://dl.acm.org/doi/10.1016/0169-7552%2894%2900012-I
Lankewicz LBenard M(1991)Real-time anomaly detection using a nonparametric pattern recognition approachProceedings Seventh Annual Computer Security Applications Conference10.1109/CSAC.1991.213016(80-89)Online publication date: 1991
https://doi.org/10.1109/CSAC.1991.213016
Raghavan SVasukiammaiyar DHaring G(1994)Generative networkload models for a single server environmentACM SIGMETRICS Performance Evaluation Review10.1145/183019.18303122:1(118-127)Online publication date: 1-May-1994
https://dl.acm.org/doi/10.1145/183019.183031
Raghavan SVasukiammaiyar DHaring GDowdy LBunt R(1994)Generative networkload models for a single server environmentProceedings of the 1994 ACM SIGMETRICS conference on Measurement and modeling of computer systems10.1145/183018.183031(118-127)Online publication date: 1-May-1994
https://dl.acm.org/doi/10.1145/183018.183031

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