Visual data mining methods for kernel smoothed estimates of cox processes

Rohde, David, Huang, Ruth, Corcoran, Jonathan and White, Gentry (2013). Visual data mining methods for kernel smoothed estimates of cox processes. In: Jiuyong Li, Longbing Cao, Can Wang, Kay Chen Tan, Bo Liu, Jian Pei and Vincent S. Tseng, Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2013 International Workshops: DMApps, DANTH, QIMIE, BDM, CDA, CloudSD, Revised Selected Papers. 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013, Gold Coast, QLD Australia, (83-94). 14 - 17 April 2013. doi:10.1007/978-3-642-40319-4_8


Author Rohde, David
Huang, Ruth
Corcoran, Jonathan
White, Gentry
Title of paper Visual data mining methods for kernel smoothed estimates of cox processes
Conference name 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013
Conference location Gold Coast, QLD Australia
Conference dates 14 - 17 April 2013
Proceedings title Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2013 International Workshops: DMApps, DANTH, QIMIE, BDM, CDA, CloudSD, Revised Selected Papers   Check publisher's open access policy
Journal name Lecture Notes in Computer Science   Check publisher's open access policy
Place of Publication Heidelberg, German
Publisher Springer
Publication Year 2013
Year available 2013
Sub-type Fully published paper
DOI 10.1007/978-3-642-40319-4_8
Open Access Status
ISBN 9783642403187
9783642403194
ISSN 0302-9743
1611-3349
Editor Jiuyong Li
Longbing Cao
Can Wang
Kay Chen Tan
Bo Liu
Jian Pei
Vincent S. Tseng
Volume 7867
Start page 83
End page 94
Total pages 12
Collection year 2014
Language eng
Abstract/Summary Real world planning of complex logistical organisations such as the fire service is a complex task requiring synthesis of many different computational techniques, from artificial intelligence and statistical or machine learning to geographical information systems and visualization. A particularly promising approach is to apply established data mining techniques in order to produce a model and make forecasts. The nature of the forecast can then be rendered using visualization techniques in order to assess operational decisions, simultaneously benefiting from generic and powerful data mining techniques, and using visualization to understand these results in the context of the actual problem of interest which may be very specific. Previous approaches to visualization in similar contexts use iso surfaces to visualize densities, these methods ignore recent improvements in interactive 3D visualization such as volume rendering and cut-planes, these methods also ignore what is often a key problem of interest comparing two different stochastic processes, finally previous methods have not paid sufficient attention to differences between estimation of densities and point processes (or Cox processes). This paper seeks to address all of these shortcomings and make recommendations for the trade-offs between visualization techniques for operational decision making. Finally we also demonstrate the ability to include interactive 3D plots within a paper by rendering an iso surface using 3D portable document format (PDF).
Subjects 1700 Computer Science
2614 Theoretical Computer Science
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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