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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280
13
章 時系列クラスタリング
13.1
心電図データ
時系列クラスタリング問題をわかりやすくするために、実世界の特定の問題を考えてみよう。医療機
関に勤めていて、心電図（
ECG
：
electrocardiogram
）を分析しなければならないとしよう。心電計は、
皮膚上に設置した電極を用いて、一定期間の心臓の電気的な活動を記録するものだ。心電計は通常お
よそ
10
秒の活動を記録する。心電図は心疾患検出の助けとなる。
ほとんどの心電図には正常な心拍が記録されるだけだが、もし異常があれば心不全などの心臓に有
害な事象が起こる前に、医療従事者が見つけて対応する必要がある。心電計は山と谷があるような折
れ線グラフを生成する。心電図をクラス分類するのは、単純なパターン認識タスクなので、機械学習
に適している。
実際には、心電図はあまりきれいにとれない。このため、心電図をいくつかのグループにクラス分類
することは難しく、間違いを犯しやすい。
波の振幅（
amplitude
、一番高い、もしくは一番低い場所からセンターラインへの距離）、周期
（
period
、あるピークと次のピークまでの距離）、位相シフト（
phase shift
、 水 平 シ フ ト と も ）、 垂直シフ
ト（
vertical shift
）などが変動するので、どんな機械学習ベースのクラス分類器にとっても、このような
データの学習は容易ではない。
13.2
時系列クラスタリングに対するアプローチ
時系列クラスタリングを行うなら、どのような方法をとるにせよ、こ
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.