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Data Mining - Baseball Data

Data Mining - Baseball Data. Clustering ( 群 聚 方 法 ) 學生 : 鄭嘉仁 . 蘇信嘉 指導老師 : 于昌永. 目的 :. 利用 K-mean , Pam , Hierarchical 三種群聚的方法 (Cluster) , 去分析 Baseball Data 中 , 球員的表現與薪水高低的關聯性 . 接著利用這三種方法的結果 , 去預測 Baseball Data 中某些球員的缺失薪水. 步驟 :. Standardize K-means Pam

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Data Mining - Baseball Data

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  1. Data Mining - Baseball Data Clustering ( 群 聚 方 法 ) 學生:鄭嘉仁.蘇信嘉 指導老師:于昌永

  2. 目的 : 利用K-mean , Pam , Hierarchical 三種群聚的方法(Cluster) , 去分析Baseball Data中 , 球員的表現與薪水高低的關聯性. 接著利用這三種方法的結果 , 去預測Baseball Data中某些球員的缺失薪水.

  3. 步驟 : • Standardize • K-means • Pam • Hierarchical

  4. 總結 : 因為k-mean和pam皆需選定k值,而我們可以利用Hierarchical的方法,選定最佳的k值,再去進行k-mean和pam的分析. 所以經過這三種方法的分析結果,我們選定了k=5為最佳k值.然後對k-mean跟pam作比較,結果如下:

  5. 用k-mean(k=5)下去預測缺失的薪水資料 球員編號 薪水預測 第一類 102.104.107.200.247. 50萬 第二類 16.37.43.58.67.72.78.84.95.106.139.151.158. 161.170.172.174.204.209.271.299.317 95萬 第三類 33.49.81.105.236.284.309. 15萬 第四類 19.23.31.39.42.45.53.65.70.98.115.126.145. 159.211.226.229.251.255.293.303 55萬 第五類 1.40.198.254 45萬

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