Sequential data mining methods for a missing attribute value

Xun Ge, Jianhua Gong

Research output: Contribution to journalArticlepeer-review

Abstract

In recent years, the National Bureau of Statistics of China released rankings comparing the production of industrial commodities in China and the gross domestic product (GDP) of China to the rest of the world. However, an entry is missing. As data mining for missing attribute values, this paper introduces a new method by combining sequential data mining methods and decision rules theory. By using this method, the missing entry has been lled, which improves Industrial Commodity Statistics Yearbooks.

Original languageEnglish
Pages (from-to)6851-6858
Number of pages8
JournalJournal of Computational Information Systems
Volume9
Issue number17
DOIs
Publication statusPublished - Oct 29 2013

Keywords

  • Data mining
  • Decision rule
  • Decision table
  • Gross domestic product
  • Information system
  • Output of the main industrial commodity
  • Ranking

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications

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