On data mining, compression, and Kolmogorov complexity

• Christos Faloutsos ^[1] ; Vasileios Megalooikonomou ^[2]
  1. [1] Carnegie Mellon University

     Carnegie Mellon University

     City of Pittsburgh, Estados Unidos

     
  2. [2] Temple University

     Temple University

     City of Philadelphia, Estados Unidos

     
• Localización: Data mining and knowledge discovery, ISSN 1384-5810, Vol. 15, Nº 1, 2007, págs. 3-20
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • Will we ever have a theory of data mining analogous to the relational algebra in databases? Why do we have so many clearly different clustering algorithms? Could data mining be automated? We show that the answer to all these questions is negative, because data mining is closely related to compression and Kolmogorov complexity; and the latter is undecidable. Therefore, data mining will always be an art, where our goal will be to find better models (patterns) that fit our datasets as best as possible.