Evaluations of a multiple SOMs method for estimating missing values

Kouki Arima, Nobuhiro Okada, Yasutaka Tsuji, Kazuo Kiguchi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Data mining, which is a technique to extract variable information from enormous data, becomes more and more important. Real data often has missing values. Therefore, a method for estimating the missing data is required in application of data mining. Using multiple self-organizing maps (MSOM) proposed by Kikuchi et al. is one of such estimating method. This method does not need a concrete mathematical model and is also available for nonlinear data. However the performance for various missing patterns were unclear, in addition, the comparisons with conventional imputation methods were not provided. This paper demonstrates the performance and the comparison results through simulation experiments with various missing patterns and conventional methods.

Original languageEnglish
Title of host publication2014 IEEE/SICE International Symposium on System Integration, SII 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages796-801
Number of pages6
ISBN (Electronic)9781479969449
DOIs
Publication statusPublished - Jan 30 2014
Event7th IEEE/SICE International Symposium on System Integration, SII 2014 - Tokyo, Japan
Duration: Dec 13 2014Dec 15 2014

Publication series

Name2014 IEEE/SICE International Symposium on System Integration, SII 2014

Other

Other7th IEEE/SICE International Symposium on System Integration, SII 2014
Country/TerritoryJapan
CityTokyo
Period12/13/1412/15/14

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Networks and Communications
  • Information Systems

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