مقایسه روش‌‌های مختلف تجزیه‌وتحلیل خوشه‌‌ای (مطالعه موردی: جنگل‌‌های بلوط کرمانشاه)

نوع مقاله: علمی- پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه جنگل‌‌‌داری، دانشکده منابع طبیعی، دانشگاه ارومیه، ارومیه، ایران

2 دانشیار، گروه جنگل‌‌‌‌داری، دانشکده منابع طبیعی، دانشگاه ارومیه، ارومیه، ایران

3 استادیار، گروه ریاضی، دانشکده علوم، دانشگاه ارومیه، ارومیه، ایران

4 دانشیار، گروه جنگل‌‌داری، دانشکده منابع طبیعی، دانشگاه ارومیه، ارومیه، ایران

5 استاد، گروه اکولوژی، دانشگاه ایالتی مونتانا، بوزمن، آمریکا

چکیده

طبقه‌‌بندی، ابزاری کارآمد برای پژوهش‌‌های جوامع گیاهی و بررسی پدید‌‌ه‌‌های بوم‌‌شناختی است. هدف از پژوهش پیش‌‌رو، مقایسه روش‌های مختلف خوشه‌‌بندی در تجزیه‌وتحلیل خوشه‌‌ای بود. سه قطعه جنگلی با جهت جنوبی از توده‌های بلوط در جنگل‌‌های چهارزبر استان کرمانشاه با شرایط مشابه ازنظر شیب و ارتفاع از سطح دریا انتخاب شدند. در هر قطعه در فاصله‌های صفر، 25، 50، 100 و 150 متری با استفاده از سه خط‌نمونه که در فاصله‌های 200 متری از هم قرار گرفتند، نمونه‌برداری انجام شد. در این بررسی از روش تجزیه‌وتحلیل خوشه‌‌ای برای طبقه‌‌بندی پوشش‌‌ گیاهی استفاده شد. برای محاسبه ماتریس فاصله‌‌ها از روش Gower و برای اتصال خوشه‌‌ها از چهار روش نزدیک‌‌ترین همسایه، دورترین همسایه، اتصال میانگین و اتصال وارد استفاده شد. برای یافتن تعداد بهینه خوشه‌‌ها و بررسی کیفیت خوشه‌‌بندی در روش‌‌های مختلف از معیار سیلوئت استفاده شد. همچنین، انطباق بین ماتریس فاصله محاسبه‌‌شده و دندروگرام به‌دست آمده از روش‌‌های مختلف با ضریب همبستگی کوفنتیک ارزیابی شد. نتایج نشان داد که تعداد بهینه خوشه‌‌ها در جوامع بلوط منطقه مورد مطالعه، دو خوشه بود. مقدار همبستگی کوفنتیک بین ماتریس فاصله و دندروگرام به‌دست‌آمده از روش‌های میانگین و نزدیک‌‌ترین همسایه بیشتر از دو روش وارد و دورترین همسایه به‌دست آمد. همچنین، کیفیت خوشه‌‌بندی روش‌های نزدیک‌‌ترین همسایه و میانگین بهتر از دو روش دیگر بود، اما میانگین شاخص سیلوئت در خوشه دوم روش نزدیک‌‌ترین همسایه، بسیار کم بود، بنابراین روش اتصال خوشه میانگین همراه با ضریب فاصله Gower برای داده‌‌های ترتیبی مطلوب‌‌‌تر است و تغییری در داده‌‌ها ایجاد نمی‌کند.

کلیدواژه‌ها


عنوان مقاله [English]

Comparison of different methods for cluster analysis (Case study: Kermanshah oak forest, Iran)

نویسندگان [English]

  • Naghmeh Pakgohar 1
  • Javad Eshaghi Rad 2
  • Gholam Hossein Gholami 3
  • Ahmad Alijanpour 4
  • David W. Roberts 5
1 Ph.D. Student, Department of Forestry, Faculty of Natural Resources, Urmia University, Urmia, Iran
2 Associate Prof., Department of Forestry, Faculty of Natural Resources, Urmia University, Urmia, Iran
3 Assistant Prof., Department of Mathematics, Faculty of Science, Urmia University, Urmia, Iran
4 Associate Prof., Department of Forestry, Faculty of Natural Resources, Urmia University, Urmia, Iran
5 Prof., Department of Ecology, Montana State University, Bozeman, USA
چکیده [English]

Vegetation classification is an essential tool to describe, understand, predict and manage ecosystems. The aim of this study was to compare different types of hierarchical clustering. Three forest patches with similar slope and altitude gradients located on the southern slopes of Chahar Zebar forests, Kermanshah province, were selected. Vegetation sampling in each patch was conducted at 0, 25, 50, 100 and 150-meter distances along three transects that were 200 m apart. Cluster analysis was used for the classification of samples. Amongst the applied methods, Gower’s distance (or similarity) initially computes distances between pairs of variables over data sets and then merges those distances with the nearest neighbor, complete neighbor, average neighbor, and Ward’s method. The optimal number and quality of clusters were evaluated with silhouette criteria. In addition, the Cophenetic correlation coefficient was computed for evaluating the correlation between the dendrogram and the distance matrix. Results showed that two was the optimal number of clustering for oak stands. Moreover, the Cophenetic correlation coefficient between the distance matrix and the nearest neighbor and average method was higher than that returned between complete neighbor and Ward’s method. Based on silhouette criteria, the nearest neighbor and average methods were associated with higher cluster quality compared with two other methods. However, the mean value of the silhouette index was low for the second cluster of the nearest neighbor method. Considering the disadvantages of the nearest neighbor, the average method is suggested for clustering categorical data.

کلیدواژه‌ها [English]

  • Classification
  • Clustering
  • Gower distance
  • ordinal number
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