Hierarchical Document Clustering Using Frequent Closed Sets

  • Marzena Kryszkiewicz
  • Łukasz Skonieczny
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 35)

Abstract

Aerial archaeology plays an important role in the detection and documentation of archaeological sites, which often cannot be easily seen from the ground. It is a quick way to survey large areas, but requires a lot of error-prone human work to analyze it afterwards. In this paper we utilize some of the best-performing image processing and data mining methods to develop a system capable of an accurate automated classification of such aerial photographs. The system consists of phases of image indexing, rough image segmentation, feature extraction, feature grouping and building the classifier. We present the results of experiments conducted on a real set of archaeological and non-archaeological aerial photographs and conclude with perspectives for future work.

Keywords

Feature Vector Frequent Itemsets Initial Cluster Frequent Item Document Cluster 
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 2006

Authors and Affiliations

  • Marzena Kryszkiewicz
    • 1
  • Łukasz Skonieczny
    • 1
  1. 1.Institute of Computer ScienceWarsaw University of TechnologyWarszawaPoland

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