Discovering data set nature through algorithmic clustering based on string compression Articles uri icon

publication date

  • January 2015

start page

  • 699

end page

  • 711

issue

  • 3

volume

  • 27

International Standard Serial Number (ISSN)

  • 1041-4347

Electronic International Standard Serial Number (EISSN)

  • 1558-2191

abstract

  • Text data sets can be represented using models that do not preserve text structure, or using models that preserve text structure. Our hypothesis is that depending on the data set nature, there can be advantages using a model that preserves text structure over one that does not, and vice versa. The key is to determine the best way of representing a particular data set, based on the data set itself. In this work, we proposde B''orjae to investigate this problem by combining text distortion and algorithmic clustering based on string compression. Specifically, a distortion technique previously developed by the authors is applied to destroy text structureprogressively. Following this, a clustering algorithm based on string compression is used to analyze the effects of the distortion on the information contained in the texts. Several experiments are carried out on text data sets and artificially-generated data sets. The results show that in strongly structural data sets the clustering results worsen as text structure is progressively destroyed. Besides, they show that using a compressor which enables the choice of the size of the left-context symbols helps to determine the nature of the data sets. Finally, the results are contrasted with a method based on multidimensional projections and analogous conclusions are obtained. ¬© 1989-2012 IEEE.

subjects

  • Computer Science
  • Information Science

keywords

  • compression-based text clustering data compression dendrogram silhouette coefficient multidimensional projections normalized compression distance ppmd order word removal algorithms clustering algorithms dendrograms multidimensional projections normalized compression distance ppmd order text clustering word removals data compression