Advances in Knowledge Discovery and Data Mining

Volume 6635 of the series Lecture Notes in Computer Science pp 444-456

Tracing Evolving Clusters by Subspace and Value Similarity

  • Stephan GünnemannAffiliated withData Management and Data Exploration Group, RWTH Aachen University
  • , Hardy KremerAffiliated withData Management and Data Exploration Group, RWTH Aachen University
  • , Charlotte LaufkötterAffiliated withInstitute of Biogeochemistry and Pollutant Dynamics, ETH Zürich
  • , Thomas SeidlAffiliated withData Management and Data Exploration Group, RWTH Aachen University

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Cluster tracing algorithms are used to mine temporal evolutions of clusters. Generally, clusters represent groups of objects with similar values. In a temporal context like tracing, similar values correspond to similar behavior in one snapshot in time. Each cluster can be interpreted as a behavior type and cluster tracing corresponds to tracking similar behaviors over time. Existing tracing approaches are designed for datasets satisfying two specific conditions: The clusters appear in all attributes, i.e. fullspace clusters, and the data objects have unique identifiers. These identifiers are used for tracking clusters by measuring the number of objects two clusters have in common, i.e. clusters are traced based on similar object sets.

These conditions, however, are strict: First, in complex data, clusters are often hidden in individual subsets of the dimensions. Second, mapping clusters based on similar objects sets does not reflect the idea of tracing similar behavior types over time, because similar behavior can even be represented by clusters having no objects in common. A tracing method based on similar object values is needed. In this paper, we introduce a novel approach that traces subspace clusters based on object value similarity. Neither subspace tracing nor tracing by object value similarity has been done before.