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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13.7
時系列クラスタリングアルゴリズムの比較
299
HDBSCAN
の場合は、
85
個のデータセットを実行する時間は
10
分弱だった。
13.7.4
3
つの時系列クラスタリング手法の比較
さて、これら
3
つのクラスタリングアルゴリズムを比較して、どれが最良なのかを見てみよう。比較
方法としては、訓練セットとテストセットに対する調整ランド指標をそれぞれに関して計算することが
考えられる。
それぞれのアルゴリズムのスコアを下に示す。
k-Shape Results
Train ARS 0.165139
Test ARS 0.151103
k-Means Results
Train ARS 0.184789
Test ARS 0.178960
HDBSCAN Results
Train ARS 0.178754
Test ARS 0.158238
結果は、およそ同じだが、
k
平均法の調整ランド指標が最も高い調整ランド指標を得ている。
さらに深く検証するために、それぞれのアルゴリズムのそれぞれのデータセットに対する性能が、
1
位だった場合、
2
位だった場合、
3
位だった場合を
85
個のデータセットに対して数えてみよう。
# Count top place finishes
timeSeriesClusteringDF = pd.DataFrame(data=[],index=kShapeDF.index, \
columns=["kShapeTest", \
"kMeansTest", \
"hdbscanTest"])
timeSeriesClusteringDF.kShapeTest ...
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