Exploratory hot spot profile analysis using interactive visual drill-down self-organizing maps

Denny, Graham J. Williams, Peter Christen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 12th Pacific-Asia Conference, PAKDD 2008, Proceedings
Pages536-543
Number of pages8
DOIs
Publication statusPublished - 9 Jun 2008
Event12th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2008 - Osaka, Japan
Duration: 20 May 200823 May 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5012 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2008
Country/TerritoryJapan
CityOsaka
Period20/05/0823/05/08

Keywords

  • Attribute ranking
  • Hot spot analysis
  • Imbalanced data
  • Interactive drill-down visualization
  • Self-organizing maps

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