Exploring geographical metadata by automatic and visual data mining

• Main Author: Demšar, Urška
• Format: Others
• Language: English
• Published: KTH, Infrastruktur 2004
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-1779; http://nbn-resolving.de/urn:isbn:91-7323-077-4

Metadata are data about data. They describe characteristicsand content of an original piece of data. Geographical metadatadescribe geospatial data: maps, satellite images and othergeographically referenced material. Such metadata have twocharacteristics, high dimensionality and diversity of attributedata types, which present a problem for traditional data miningalgorithms. Other problems that arise during the exploration ofgeographical metadata are linked to the expertise of the userperforming the analysis. The large amounts of metadata andhundreds of possible attributes limit the exploration for anon-expert user, which results in a potential loss ofinformation that is hidden in metadata. In order to solve some of these problems, this thesispresents an approach for exploration of geographical metadataby a combination of automatic and visual data mining. Visual data mining is a principle that involves the human inthe data exploration by presenting the data in some visualform, allowing the human to get insight into the data and torecognise patterns. The main advantages of visual dataexploration over automatic data mining are that the visualexploration allows a direct interaction with the user, that itis intuitive and does not require complex understanding ofmathematical or statistical algorithms. As a result the userhas a higher confidence in the resulting patterns than if theywere produced by computer only. In the thesis we present the Visual data mining tool (VDMtool), which was developed for exploration of geographicalmetadata for site planning. The tool provides five differentvisualisations: a histogram, a table, a pie chart, a parallelcoordinates visualisation and a clustering visualisation. Thevisualisations are connected using the interactive selectionprinciple called brushing and linking. In the VDM tool the visual data mining concept is integratedwith an automatic data mining method, clustering, which finds ahierarchical structure in the metadata, based on similarity ofmetadata items. In the thesis we present a visualisation of thehierarchical structure in the form of a snowflake graph. Keywords:visualisation, data mining, clustering, treedrawing, geographical metadata.