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Applying Compression to Hierarchical Clustering

  • Gilad Baruch
  • Shmuel Tomi KleinEmail author
  • Dana Shapira
Conference paper
  • 346 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11223)

Abstract

Hierarchical Clustering is widely used in Machine Learning and Data Mining. It stores bit-vectors in the nodes of a k-ary tree, usually without trying to compress them. We suggest a data compression application of hierarchical clustering with a double usage of the xoring operations defining the Hamming distance used in the clustering process, extending it also to be used to transform the vector in one node into a more compressible form, as a function of the vector in the parent node. Compression is then achieved by run-length encoding, followed by optional Huffman coding, and we show how the compressed file may be processed directly, without decompression.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Gilad Baruch
    • 1
  • Shmuel Tomi Klein
    • 1
    Email author
  • Dana Shapira
    • 2
  1. 1.Department of Computer ScienceBar Ilan UniversityRamat GanIsrael
  2. 2.Department of Computer ScienceAriel UniversityArielIsrael

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