April 2020
Intermediate to advanced
344 pages
8h 22m
Japanese
Content preview from Pythonではじめる教師なし学習 ―機械学習の可能性を広げるラベルなしデータの利用
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292
13
章 時系列クラスタリング
Predicted Cluster 2.0
1.0 553
4.0 16
2.0 9
3.0 7
Name: actual, dtype: int64
Predicted Cluster 3.0
2.0 35
1.0 5
4.0 5
5.0 3
3.0 3
Name: actual, dtype: int64
Predicted Cluster 4.0
1.0 30
4.0 1
3.0 1
2.0 1
Name: actual, dtype: int64
Count of Non-Primary Points: 83
それぞれのクラスタ内の心電図の大半は同じ真のラベルを持つことがわかる。このことから
k-Shape
法が分割したクラスタはうまく分類され、均質であると言える。
13.5
k
平均法を用いた
ECG5000
に対する時系列クラスタリング
完全を期すため、
k-Shape
法の結果を
k
平均法の結果と比較してみよう。
訓練には
tslearn
パッケージを用い、評価には以前と同様に調整ランド指標を用いる。
クラスタ数を
5
、一度の実行での最大繰り返し回数を
100
、実行回数を
100
とし、距離尺度をユーク
リッド距離とし、乱数シードを
2019
とする。
# Train using Time Series k-Means
km = TimeSeriesKMeans(n_clusters=5, max_iter=100, n_init=100, \
metric="euclidean", ...
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