Combining fuzzy sammon mapping and fuzzy clustering approach to perform clustering effect analysis: Take the banking service satisfaction as an example

  • Authors:

  • Wen-Tsao Pan

  • Affiliations:

  • Department of Information Management, Oriental Institute of Technology 3F., No. 12, Lane 271, Longjiang Rd., Jhongshan District, Taipei City 104, Taiwan, ROC

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2010

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Abstract

Hard clustering and fuzzy clustering analysis is the basis of the construction of many classifications and systems with its main focus on planning and dividing data into many subsets according to certain rules. Due to the practical function of clustering analysis, many researchers thus proposed different clustering algorithms to be used by researchers around the world. In this article, Fuzzy sammon Mapping method is implemented to perform clustering effect and classification capability analysis on these frequently used clustering algorithms. From the result of test data of an investigation of banking service satisfaction, GK Cluster algorithm was found to have very good clustering effect; however, as for classification capability, hard clustering analysis method has proved to be the better approach amongst the two.