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

Denny*, Graham J. Williams, Peter Christen

*Corresponding author for this work

    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 - 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

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