Kernel K Medoids Algorithm with Selected Initial Values

  • 方 茜

Student thesis: Master's Thesis


This study proposes a clustering algorithm that combine gaussian kernel function with k medoids clustering algorithm In the meanwhile we use a variable called Vj (Park and Jun 2009) to rank objects and select the r middle values as our initial centers The selection of initial values makes the clustering process more efficient and the combination of gaussian kernel function makes the clustering outcome more resistant to outliers or noises To evaluate the proposed algorithm we analyze some real synthetic and relational datasets and compar- ing with the results of other algorithms in terms of the Adjusted Rand Index F1 score and Mean Squared Error The outcomes show that our proposed algorithm having better cluster- ing performance over the other mentioned algorithms (k means k medoids) in this study
Date of Award2017 Jun 19
Original languageEnglish
SupervisorMiin-Jye Wen (Supervisor)

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