A Data Classification Method Using Genetic Algorithm and K-Means Algorithm with Optimizing Initial Cluster Center

Haobin Shi, Meng Xu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

21 Scopus citations

Abstract

Aiming at the problems of the classical data classification method, this paper proposes a method using genetic algorithm and K-means algorithm to classify data. In order to improve the effectiveness of data analysis, considering that the classical K-means algorithm is easy to be influenced by the initial cluster center with random selection, this paper improves the K-means algorithm by using the method of optimizing the initial cluster center. This paper first uses the sorted neighborhood method (SNM) to preprocess the data, and then the K-means algorithm is used to cluster data. In order to improve the accuracy of the K-means algorithm, this paper optimizes the initial cluster center, and unifies the genetic algorithm for the data dimensionality reduction. The experimental results show that the proposed method has higher classification accuracy than the classical data classification method has.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Computer and Communication Engineering Technology, CCET 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages224-228
Number of pages5
ISBN (Electronic)9781538674376
DOIs
StatePublished - 20 Nov 2018
Event2018 IEEE International Conference on Computer and Communication Engineering Technology, CCET 2018 - Beijing, China
Duration: 18 Aug 201820 Aug 2018

Publication series

Name2018 IEEE International Conference on Computer and Communication Engineering Technology, CCET 2018

Conference

Conference2018 IEEE International Conference on Computer and Communication Engineering Technology, CCET 2018
Country/TerritoryChina
CityBeijing
Period18/08/1820/08/18

Keywords

  • data classification
  • genetic algorithm
  • K-means algorithm
  • sorted neighborhood method

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