HSM: Heterogeneous Subspace Mining in High Dimensional Data

  • Emmanuel Müller
  • Ira Assent
  • Thomas Seidl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5566)


Heterogeneous data, i.e. data with both categorical and continuous values, is common in many databases. However, most data mining algorithms assume either continuous or categorical attributes, but not both. In high dimensional data, phenomena due to the “curse of dimensionality” pose additional challenges. Usually, due to locally varying relevance of attributes, patterns do not show across the full set of attributes.

In this paper we propose HSM, which defines a new pattern model for heterogeneous high dimensional data. It allows data mining in arbitrary subsets of the attributes that are relevant for the respective patterns. Based on this model we propose an efficient algorithm, which is aware of the heterogeneity of the attributes. We extend an indexing structure for continuous attributes such that HSM indexing adapts to different attribute types. In our experiments we show that HSM efficiently mines patterns in arbitrary subspaces of heterogeneous high dimensional data.


High Dimensional Data Continuous Attribute Frequent Itemset Heterogeneous Data Categorical Attribute 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Emmanuel Müller
    • 1
  • Ira Assent
    • 2
  • Thomas Seidl
    • 1
  1. 1.Data management and exploration groupRWTH Aachen UniversityGermany
  2. 2.Department of Computer ScienceAalborg UniversityDenmark

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