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標題Title: Learning Scaling Coefficient in Possibilistic Latent Variable Algorithm from Complex Diagnosis Data
作者Authors: 鄞宗賢
上傳單位Department: 多媒體與電腦娛樂科學系
上傳時間Date: 2009-11-30
上傳者Author: 鄞宗賢
審核單位Department: 多媒體與電腦娛樂科學系
審核老師Teacher: 鄞宗賢
檔案類型Categories: 論文Thesis
關鍵詞Keyword: bioinformatics, clustering, machine learning, latent variable
摘要Abstract: The Possibilistic Latent Variable (PLV) clustering
algorithm is a powerful tool for the analysis of complex datasets
due to its robustness toward data distributions of different
types and its ability to accurately identify the inherent clusters
within the data. The scaling coefficient in the PLV algorithm
plays a key role in reducing the effects of noise, thereby
improving the precision of the clustering results. However, the
optimal value of the scaling parameter varies depending on the
population type of dataset. Accordingly, the current study
proposes an evaluation method for evaluating suitable values
of the scaling parameter. The relative comparison of each
method is then examined by conducting PLV clustering trials
using datasets comprising data of different types and patterns.

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2009_11_c593f92a.pdf 243Kb pdf 441 114
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