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ผลงานตีพิมพ์ในวารสารวิชาการ

Comparison of Clustering Techniques for Cluster Analysis

ผู้แต่ง:Imgปิยธิดา รุจะศิริ, ImgDr.Boonorm Chomtee, Associate Professor,

วารสาร:

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ที่มา:วิทยาสารเกษตรศาสตร์ สาขา วิทยาศาสตร์

หัวเรื่อง:ไม่มีชื่อไทย (ชื่ออังกฤษ : Comparison of Clustering Techniques for Cluster Analysis)

ผู้เขียน:Imgปิยธิดา รุจะศิริ, Imgดร.บุญอ้อม โฉมที, รองศาสตราจารย์

สื่อสิ่งพิมพ์:pdf

Abstract

Cluster analysis is important for analyzing the number of clusters of natural data in several domains. Various clustering methods have been proposed. However, it is very difficult to choose the method best suited to the type of data. Therefore, the objective of this research was to compare the effectiveness of five clustering techniques with multivariate data. The techniques were: hierarchical clustering method; K-means clustering algorithm; Kohonen’s Self-Organizing Maps method (SOM); K-medoids method; and K-medoids method integrated with Dynamic Time Warping distance measure (DTW). To evaluate these five techniques, the root mean square standard deviation (RMSSTD) and r2 (RS) were used. For RMSSTD, a lower value indicates a better technique and for RS, a higher value indicates a better technique. These approaches were evaluated using both real and simulated data which were multivariate normally distributed. Each dataset was generated by a Monte Carlo technique with 100 sample sizes and repeated 1,000 times for 3, 5 and 7 variables. In this research, 2, 3, 4, 5, 6, 7 and 8 clusters were studied. Both real and simulated datasets provided the same result, with the K-means clustering method having the closest RMSSTD and RS results to the SOM method. These two methods yielded the lowest RMSSTD and highest RS in all simulations. Hence, both K-means and SOM were considered to be the most suitable techniques for cluster analysis.

Article Info
Agriculture and Natural Resources -- formerly Kasetsart Journal (Natural Science), Volume 043, Issue 2, Apr 09 - Jun 09, Page 378 - 388 |  PDF |  Page 

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Researcher

ดร. บุญอ้อม โฉมที, รองศาสตราจารย์

ที่ทำงาน:ภาควิชาสถิติ คณะวิทยาศาสตร์ บางเขน

สาขาที่สนใจ:Statistical Analysis; Response Surface Methodology; Experimental Design; Regression Analysis

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