Article

On the classification of interactive user behaviour indices

Authors:

S. V. Raghavan,

R. KalyanakrishnanAuthors Info & Claims

SIGMETRICS '85: Proceedings of the 1985 ACM SIGMETRICS conference on Measurement and modeling of computer systems

Pages 40 - 48

https://doi.org/10.1145/317795.317809

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

View all

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
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
Show More Cited By

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

Recommendations

On the classification of interactive user behaviour indices

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 ...
Analysing user behaviour through dynamic population models
CHI EA '13: CHI '13 Extended Abstracts on Human Factors in Computing Systems

We apply a statistical modelling-based approach to exploring, analysing and predicting behavioural patterns of users of mobile software. The technique employed represents the behaviour of each user through a weighted mixture over data-generating ...
Behaviour Models-Enriched User Personas for Digital Behaviour Change Interventions
PETRA '24: Proceedings of the 17th International Conference on PErvasive Technologies Related to Assistive Environments

User Persona is a structured Interaction Design method for representing a group of users as conceptual models in text and pictorial formats. The regular user Persona descriptions are limited for designing Digital Behaviour Change Interventions for ...

Comments

Information & Contributors

Information

Published In

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

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

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

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Article

Acceptance Rates

Overall Acceptance Rate 459 of 2,691 submissions, 17%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 11 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

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
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

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

On the classification of interactive user behaviour indices

Analysing user behaviour through dynamic population models

Behaviour Models-Enriched User Personas for Digital Behaviour Change Interventions