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Exploratory Hot Spot Profile Analysis Using Interactive Visual Drill-Down Self-Organizing Maps

Denny, Denny; Williams, Graham; Christen, Peter

Description

Real-life datasets often contain small clusters of unusual sub-populations. These clusters, or 'hot spots', are usually sparse and of special interest to an analyst. We present a methodology for identifying hot spots and ranking attributes that distinguish them interactively, using visual drill-down Self-Organizing Maps. The methodology is particularly useful for understanding hot spots in high dimensional datasets. Our approach is demonstrated using a large real life taxation dataset.

dc.contributor.authorDenny, Denny
dc.contributor.authorWilliams, Graham
dc.contributor.authorChristen, Peter
dc.coverage.spatialOsaka Japan
dc.date.accessioned2015-12-10T21:53:42Z
dc.date.createdMay 20-23 2008
dc.identifier.isbn9783540681243
dc.identifier.urihttp://hdl.handle.net/1885/38613
dc.description.abstractReal-life datasets often contain small clusters of unusual sub-populations. These clusters, or 'hot spots', are usually sparse and of special interest to an analyst. We present a methodology for identifying hot spots and ranking attributes that distinguish them interactively, using visual drill-down Self-Organizing Maps. The methodology is particularly useful for understanding hot spots in high dimensional datasets. Our approach is demonstrated using a large real life taxation dataset.
dc.publisherSpringer
dc.relation.ispartofseriesPacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2008)
dc.sourceAdvances in Knowledge Discovery and Data Mining 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining Proceedings
dc.subjectKeywords: Cluster analysis; Data visualization; Interactive computer systems; Problem solving; Visual servoing; Attribute ranking; Hot spot analysis; Imbalanced data; Interactive drill-down visualization; Self organizing maps Attribute ranking; Hot spot analysis; Imbalanced data; Interactive drill-down visualization; Self-organizing maps
dc.titleExploratory Hot Spot Profile Analysis Using Interactive Visual Drill-Down Self-Organizing Maps
dc.typeConference paper
local.description.notesImported from ARIES
local.description.refereedYes
dc.date.issued2008
local.identifier.absfor080104 - Computer Vision
local.identifier.ariespublicationU3594520xPUB164
local.type.statusPublished Version
local.contributor.affiliationDenny, Denny, College of Engineering and Computer Science, ANU
local.contributor.affiliationWilliams, Graham, College of Engineering and Computer Science, ANU
local.contributor.affiliationChristen, Peter, College of Engineering and Computer Science, ANU
local.description.embargo2037-12-31
local.bibliographicCitation.startpage536
local.bibliographicCitation.lastpage543
local.identifier.doi10.1007/978-3-540-68125-0_48
dc.date.updated2015-12-09T07:19:57Z
local.identifier.scopusID2-s2.0-44649152313
CollectionsANU Research Publications

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