Clustering and Labeling of Multi-dimensional Mixed Structured Data

  • Marco Brambilla
  • Massimiliano Zanoni
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7538)


Cluster Analysis consists of the aggregation of data items of a given set into subsets based on some similarity properties. Clustering techniques have been applied in many fields which typically involve a large amount of complex data. This study focuses on what we call multi-domain clustering and labeling, i.e. a set of techniques for multi-dimensional structured mixed data clustering. The work consists of studying the best mix of clustering techniques that address the problem in the multi-domain setting. Considered data types are numerical, categorical and textual. All of them can appear together within the same clustering scenario. We focus on k-means and agglomerative hierarchical clustering methods based on a new distance function we define for this specific setting. The proposed approach has been validated on some real and realistic data-sets based onto college, automobile and leisure fields. Experimental data allowed to evaluate the effectiveness of the different solutions, both for clustering and labeling.


Textual Attribute Cluster Technique Numerical Attribute Categorical Attribute Vector Space Model 
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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© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Marco Brambilla
    • 1
  • Massimiliano Zanoni
    • 1
  1. 1.Dipartimento di Elettronica e InformazionePolitecnico di MilanoMilanoItaly

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