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いちばん簡単な
クラスタリング
~レッツゴーデベロッパー2011~

         真幡康徳 - @mahata
アジェンダ
●
  自己紹介
●
  クラスタリングとは
●
  にばんめに簡単な
  クラスタリング
●
  いちばん簡単な
  クラスタリング
自己紹介
真幡康徳 (@mahata)




2011年04月~: 仙台市民
今までの仕事
●
 ブログサービス
●
 レコメンドエンジン
●
 ウェブ検索
●
 ウェブ広告配信
●etc...
クラスタリング
  とは
http://www.flickr.com/photos/tonyjcase/2411318874/




クラスタリング=グルーピング
にばんめに簡単な
 クラスタリング
5分間クラスタリング




 ...正直5分でする話じゃない
データセット例
             リバウンド力   シュート力
桜木 花道 (x1)      5        1
魚住 純 (x2)       4        2
神 宗一郎 (x3)      1        5
河田 雅史 (x4)      5        4
仙道 彰 (x5)       5        5

※ 実在の漫画・キャラクタとは一切関係ありません
リバウンド力 シュート力
x1                 5         1
x2                 4         2
x3                 1         5
x4                 5         4
x5                 5         5

                            非類似度行列
x1とx2の非類似度                  x1 x2 x3 x4 x5
(ユークリッド距離)             x1     0  2 32  9 16
(x1[0] – x2[0])2       x2     2  0 18  5 10
                       x3    32 18  0 16 16
+                      x4     9  5 16  0  1
(x1[1] - x2[1])2       x5    16 10 16  1  0
リバウンド力 シュート力
x1                 5         1
x2                 4         2
x3                 1         5
x4                 5         4
x5                 5         5

                            非類似度行列
x1とx2の非類似度                  x1 x2 x3 x4 x5
(ユークリッド距離)             x1     0  2 32  9 16
(x1[0] – x2[0])2       x2     2  0 18  5 10
                       x3    32 18  0 16 16
+                      x4     9  5 16  0  1
(x1[1] - x2[1])2       x5    16 10 16  1  0
リバウンド力 シュート力
x1                 5         1
x2                 4         2
x3                 1         5
x4                 5         4
x5                 5         5

                            非類似度行列
x1とx2の非類似度                  x1 x2 x3 x4 x5
(ユークリッド距離)             x1     0  2 32  9 16
(x1[0] – x2[0])2       x2     2  0 18  5 10
                       x3    32 18  0 16 16
+
(x1[1] - x2[1])2
                       x4
                              対称行列 0 1
                              9  5 16
                       x5    16 10 16  1  0
非類似度行列
     x1 x2 x3 x4 x5
x1     0  2 32  9 16
x2     2  0 18  5 10
x3    32 18  0 16 16
x4     9  5 16  0  1
x5    16 10 16  1  0
非類似度行列
         x1 x2 x3 x4 x5
    x1     0  2 32  9 16
    x2     2  0 18  5 10
    x3    32 18  0 16 16
    x4     9  5 16  0  1
    x5    16 10 16  1  0   最小

=> x4とx5を併合する(G6)
非類似度行列
     x1 x2 x3 x4 x5
x1     0  2 32  9 16
x2     2  0 18  5 10
x3    32 18  0 16 16
x4     9  5 16  0  1
x5    16 10 16  1  0



                            x1 x2 x3 G6
                       x1     0  2 32  9
                       x2     2  0 18  5
G6の要素はx4とx5の           x3    32 18  0 16
平均を採用(群間平均法)           G6    12  7 16  0
非類似度行列
         x1 x2 x3 G6
    x1     0  2 32  9

                        最小
    x2     2  0 18  5
    x3    32 18  0 16
    G6    12  7 16  0



=> x1とx2を併合する(G7)
繰り返す(略)
樹状図
x1
x2               G8
x4
            G7        G9
x5     G6
x3

     クラスタリングの進行
にばんめに簡単な
 クラスタリング
いちばん簡単な
クラスタリング
ツールを使う




http://www.cs.waikato.ac.nz/ml/weka/
いちばん簡単な
クラスタリング
まとめ
●
    クラスタリングは怖くない
●
  クラスタリングは怖くない
●
  ナイーブな実装は簡単
●
  クラスタリングは怖くない
●
  ナイーブな実装は簡単
●
  ツールを使えばもっと簡単
参考図書
●   [1]データマイニングの基礎
ISBN-10: 4274203484
●   [2]スラムダンク 19巻
ISBN-10: 4088716299
●   [3]魁! 男塾 17巻
ISBN-10: 4088524977
おしまい

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Let's Go Developer 2011 - Clustering