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# 介绍聚类 # 介绍聚类
聚类是一种无监督学习，它假定数据集未标记或其输入与预定义的输出不匹配。它使用各种算法对未标记的数据进行排序，并根据它在数据中识别的模式提供分组。 聚类是一种无监督学习，它假定数据集未标记或其输入与预定义的输出不匹配。它使用各种算法对未标记的数据进行排序，并根据它在数据中识别的模式提供分组。
[![No One Like You by PSquare](https://img.youtube.com/vi/ty2advRiWJM/0.jpg)](https://youtu.be/ty2advRiWJM "No One Like You by PSquare") [![No One Like You by PSquare](https://img.youtube.com/vi/ty2advRiWJM/0.jpg)](https://youtu.be/ty2advRiWJM "No One Like You by PSquare")
> 🎥 点击上面的图片观看视频。当您通过聚类学习机器学习时，请欣赏一些尼日利亚舞厅曲目 - 这是 2014 年 PSquare 上高度评价的歌曲。 > 🎥 点击上面的图片观看视频。当您通过聚类学习机器学习时，请欣赏一些尼日利亚舞厅曲目 - 这是 2014 年 PSquare 上高度评价的歌曲。
## [课前测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/27/) ## [课前测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/27/)
### 介绍 ### 介绍
[聚类](https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-30164-8_124)对于数据探索非常有用。让我们看看它是否有助于发现尼日利亚观众消费音乐的趋势和模式。 [聚类](https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-30164-8_124)对于数据探索非常有用。让我们看看它是否有助于发现尼日利亚观众消费音乐的趋势和模式。
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![most popular](../images/all-genres.png) ![most popular](../images/all-genres.png)
1. 到目前为止，前三大流派主导了这个数据集。让我们专注于`afro dancehall`, `afropop`, 和`nigerian pop`，另外过滤数据集以删除任何具有 0 流行度值的内容（这意味着它在数据集中没有被归类为流行度并且可以被视为我们的目的的噪音）： 1. 到目前为止，前三大流派主导了这个数据集。让我们专注于 `afro dancehall`，`afropop` 和 `nigerian pop`，另外过滤数据集以删除任何具有 0 流行度值的内容（这意味着它在数据集中没有被归类为流行度并且可以被视为我们的目的的噪音）：
```python ```python
df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')] df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]

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## 说明 ## 说明
在本节课中，您使用了一些可视化技术来掌握绘制数据图，为聚类数据做准备。散点图在寻找一组对象时尤其有用。研究不同的方法和不同的库来创建散点图，并在notebook上记录你的工作。你可以使用这节课的数据，其他课的数据，或者你自己的数据(但是，请把它的来源记在你的notebook上)。用散点图绘制一些数据，并解释你的发现。 在本节课中，您使用了一些可视化技术来掌握绘制数据图，为聚类数据做准备。散点图在寻找一组对象时尤其有用。研究不同的方法和不同的库来创建散点图，并在 notebook 上记录你的工作。你可以使用这节课的数据，其他课的数据，或者你自己的数据（但是，请把它的来源记在你的 notebook 上）。用散点图绘制一些数据，并解释你的发现。
## 评判规则 ## 评判规则

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@ -244,7 +244,7 @@ Hint: Try to scale your data. There's commented code in the notebook that adds s
Take a look at a K-Means Simulator [such as this one](https://user.ceng.metu.edu.tr/~akifakkus/courses/ceng574/k-means/). You can use this tool to visualize sample data points and determine its centroids. You can edit the data's randomness, numbers of clusters and numbers of centroids. Does this help you get an idea of how the data can be grouped? Take a look at a K-Means Simulator [such as this one](https://user.ceng.metu.edu.tr/~akifakkus/courses/ceng574/k-means/). You can use this tool to visualize sample data points and determine its centroids. You can edit the data's randomness, numbers of clusters and numbers of centroids. Does this help you get an idea of how the data can be grouped?
Also, take a look at [this handout on k-means](https://stanford.edu/~cpiech/cs221/handouts/kmeans.html) from Stanford. Also, take a look at [this handout on K-Means](https://stanford.edu/~cpiech/cs221/handouts/kmeans.html) from Stanford.
## Assignment ## Assignment

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Dare un'occhiata a un simulatore di K-Means [tipo questo](https://user.ceng.metu.edu.tr/~akifakkus/courses/ceng574/k-means/). È possibile utilizzare questo strumento per visualizzare i punti dati di esempio e determinarne i centroidi. Questo aiuta a farsi un'idea di come i dati possono essere raggruppati? Dare un'occhiata a un simulatore di K-Means [tipo questo](https://user.ceng.metu.edu.tr/~akifakkus/courses/ceng574/k-means/). È possibile utilizzare questo strumento per visualizzare i punti dati di esempio e determinarne i centroidi. Questo aiuta a farsi un'idea di come i dati possono essere raggruppati?
Inoltre, dare un'occhiata a [questa dispensa sui k-means](https://stanford.edu/~cpiech/cs221/handouts/kmeans.html) di Stanford. Inoltre, dare un'occhiata a [questa dispensa sui K-Means](https://stanford.edu/~cpiech/cs221/handouts/kmeans.html) di Stanford.
## Compito ## Compito

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[such as this one](https://user.ceng.metu.edu.tr/~akifakkus/courses/ceng574/k-means/)같은 K-Means 시뮬레이터를 찾아봅니다. 이 도구로 샘플 데이터 포인트를 시각화하고 무게 중심을 결정할 수 있습니다. 데이터의 랜덤성, 클러스터 수와 무게 중심 수를 고칠 수 있습니다. 데이터를 그룹으로 묶기 위한 아이디어를 얻는 게 도움이 되나요? [such as this one](https://user.ceng.metu.edu.tr/~akifakkus/courses/ceng574/k-means/)같은 K-Means 시뮬레이터를 찾아봅니다. 이 도구로 샘플 데이터 포인트를 시각화하고 무게 중심을 결정할 수 있습니다. 데이터의 랜덤성, 클러스터 수와 무게 중심 수를 고칠 수 있습니다. 데이터를 그룹으로 묶기 위한 아이디어를 얻는 게 도움이 되나요?
또한, Stanford의 [this handout on k-means](https://stanford.edu/~cpiech/cs221/handouts/kmeans.html)을 찾아봅니다. 또한, Stanford 의 [this handout on K-Means](https://stanford.edu/~cpiech/cs221/handouts/kmeans.html) 을 찾아봅니다.
## 과제 ## 과제

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看看[像这样](https://user.ceng.metu.edu.tr/~akifakkus/courses/ceng574/k-means/)的 K-Means 模拟器。您可以使用此工具来可视化样本数据点并确定其质心。您可以编辑数据的随机性、聚类数和质心数。这是否有助于您了解如何对数据进行分组？ 看看[像这样](https://user.ceng.metu.edu.tr/~akifakkus/courses/ceng574/k-means/)的 K-Means 模拟器。您可以使用此工具来可视化样本数据点并确定其质心。您可以编辑数据的随机性、聚类数和质心数。这是否有助于您了解如何对数据进行分组？
另外，看看斯坦福大学的[k-means 讲义](https://stanford.edu/~cpiech/cs221/handouts/kmeans.html)。 另外，看看斯坦福大学的 [K-Means 讲义](https://stanford.edu/~cpiech/cs221/handouts/kmeans.html)。
## 作业 ## 作业

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![A turntable](./images/turntable.jpg) ![A turntable](./images/turntable.jpg)
Photo by <a href="https://unsplash.com/@marcelalaskoski?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Marcela Laskoski</a> on <a href="https://unsplash.com/s/photos/nigerian-music?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a> > Photo by <a href="https://unsplash.com/@marcelalaskoski?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Marcela Laskoski</a> on <a href="https://unsplash.com/s/photos/nigerian-music?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
In this series of lessons, you will discover new ways to analyze data using clustering techniques. Clustering is particularly useful when your dataset lacks labels. If it does have labels, then classification techniques such as those you learned in previous lessons might be more useful. But in cases where you are looking to group unlabelled data, clustering is a great way to discover patterns. In this series of lessons, you will discover new ways to analyze data using clustering techniques. Clustering is particularly useful when your dataset lacks labels. If it does have labels, then classification techniques such as those you learned in previous lessons might be more useful. But in cases where you are looking to group unlabelled data, clustering is a great way to discover patterns.
> There are useful low-code tools that can help you learn about working with clustering models. Try [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-clustering-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa) > There are useful low-code tools that can help you learn about working with clustering models. Try [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-clustering-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
## Lessons ## Lessons
1. [Introduction to clustering](1-Visualize/README.md) 1. [Introduction to clustering](1-Visualize/README.md)
2. [K-Means clustering](2-K-Means/README.md) 2. [K-Means clustering](2-K-Means/README.md)
## Credits ## Credits
These lessons were written with 🎶 by [Jen Looper](https://www.twitter.com/jenlooper) with helpful reviews by [Rishit Dagli](https://rishit_dagli) and [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan). These lessons were written with 🎶 by [Jen Looper](https://www.twitter.com/jenlooper) with helpful reviews by [Rishit Dagli](https://rishit_dagli) and [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan).

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@ -8,7 +8,7 @@ Il pubblico eterogeneo della Nigeria ha gusti musicali diversi. Usando i dati re
![Un giradischi](../images/turntable.jpg) ![Un giradischi](../images/turntable.jpg)
Foto di <a href="https://unsplash.com/@marcelalaskoski?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Marcela Laskoski</a> su <a href="https://unsplash.com/s/photos/nigerian-music?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a> > Foto di <a href="https://unsplash.com/@marcelalaskoski?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Marcela Laskoski</a> su <a href="https://unsplash.com/s/photos/nigerian-music?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
In questa serie di lezioni si scopriranno nuovi modi per analizzare i dati utilizzando tecniche di clustering. Il clustering è particolarmente utile quando l'insieme di dati non ha etichette. Se ha etichette, le tecniche di classificazione come quelle apprese nelle lezioni precedenti potrebbero essere più utili. Ma nei casi in cui si sta cercando di raggruppare dati senza etichetta, il clustering è un ottimo modo per scoprire i modelli. In questa serie di lezioni si scopriranno nuovi modi per analizzare i dati utilizzando tecniche di clustering. Il clustering è particolarmente utile quando l'insieme di dati non ha etichette. Se ha etichette, le tecniche di classificazione come quelle apprese nelle lezioni precedenti potrebbero essere più utili. Ma nei casi in cui si sta cercando di raggruppare dati senza etichetta, il clustering è un ottimo modo per scoprire i modelli.

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![A turntable](../images/turntable.jpg) ![A turntable](../images/turntable.jpg)
Photo by <a href="https://unsplash.com/@marcelalaskoski?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Marcela Laskoski</a> on <a href="https://unsplash.com/s/photos/nigerian-music?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a> > Photo by <a href="https://unsplash.com/@marcelalaskoski?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Marcela Laskoski</a> on <a href="https://unsplash.com/s/photos/nigerian-music?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
이 강의의 시리즈에서, clustering 기술로 데이터를 분석하는 새로운 방식을 찾아볼 예정입니다. Clustering 은 데이터셋에 라벨이 없으면 더욱 더 유용합니다. 만약 라벨이 있다면, 이전 강의에서 배운대로 classification 기술이 더 유용할 수 있습니다. 그러나 라벨링되지 않은 데이터를 그룹으로 묶으려면, clustering 은 패턴을 발견하기 위한 좋은 방식입니다. 이 강의의 시리즈에서, clustering 기술로 데이터를 분석하는 새로운 방식을 찾아볼 예정입니다. Clustering 은 데이터셋에 라벨이 없으면 더욱 더 유용합니다. 만약 라벨이 있다면, 이전 강의에서 배운대로 classification 기술이 더 유용할 수 있습니다. 그러나 라벨링되지 않은 데이터를 그룹으로 묶으려면, clustering 은 패턴을 발견하기 위한 좋은 방식입니다.

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![Поворотный стол](./images/turntable.jpg) ![Поворотный стол](./images/turntable.jpg)
Фото <a href="https://unsplash.com/@marcelalaskoski?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText"> Марсела Ласкоски </a> на <a href="https://unsplash.com/s/photos/nigerian-music?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText"> Unsplash </a> > Фото <a href="https://unsplash.com/@marcelalaskoski?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText"> Марсела Ласкоски </a> на <a href="https://unsplash.com/s/photos/nigerian-music?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText"> Unsplash </a>
В этой серии уроков вы откроете для себя новые способы анализа данных с помощью методов кластеризации. Кластеризация особенно полезна, когда в наборе данных отсутствуют метки. Если на нем есть ярлыки, тогда могут быть более полезными методы классификации, подобные тем, которые вы изучили на предыдущих уроках. Но в случаях, когда вы хотите сгруппировать немаркированные данные, кластеризация - отличный способ обнаружить закономерности. В этой серии уроков вы откроете для себя новые способы анализа данных с помощью методов кластеризации. Кластеризация особенно полезна, когда в наборе данных отсутствуют метки. Если на нем есть ярлыки, тогда могут быть более полезными методы классификации, подобные тем, которые вы изучили на предыдущих уроках. Но в случаях, когда вы хотите сгруппировать немаркированные данные, кластеризация - отличный способ обнаружить закономерности.

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# 机器学习中的聚类模型 # 机器学习中的聚类模型
聚类（clustering)是一项机器学习任务，用于寻找类似对象并将他们分成不同的组（这些组称做“聚类”(cluster)）。聚类与其它机器学习方法的不同之处在于聚类是自动进行的。事实上，我们可以说它是监督学习的对立面。 聚类（clustering）是一项机器学习任务，用于寻找类似对象并将他们分成不同的组（这些组称做“聚类”（cluster））。聚类与其它机器学习方法的不同之处在于聚类是自动进行的。事实上，我们可以说它是监督学习的对立面。
## 本节主题: 尼日利亚观众音乐品味的聚类模型🎧 ## 本节主题: 尼日利亚观众音乐品味的聚类模型🎧
@ -8,15 +8,17 @@
![A turntable](../images/turntable.jpg) ![A turntable](../images/turntable.jpg)
<a href="https://unsplash.com/@marcelalaskoski?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Marcela Laskoski</a>在<a href="https://unsplash.com/s/photos/nigerian-music?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>上的照片 > <a href="https://unsplash.com/@marcelalaskoski?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Marcela Laskoski</a> 在 <a href="https://unsplash.com/s/photos/nigerian-music?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a> 上的照片
在本系列课程中，您将发现使用聚类技术分析数据的新方法。当数据集缺少标签的时候，聚类特别有用。如果它有标签，那么分类技术(比如您在前面的课程中所学的那些)可能会更有用。但是如果要对未标记的数据进行分组，聚类是发现模式的好方法。 在本系列课程中，您将发现使用聚类技术分析数据的新方法。当数据集缺少标签的时候，聚类特别有用。如果它有标签，那么分类技术(比如您在前面的课程中所学的那些)可能会更有用。但是如果要对未标记的数据进行分组，聚类是发现模式的好方法。
> 这里有一些有用的低代码工具可以帮助您了解如何使用聚类模型。尝试 [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-clustering-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa) > 这里有一些有用的低代码工具可以帮助您了解如何使用聚类模型。尝试 [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-clustering-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
## 课程安排 ## 课程安排
1. [介绍聚类](../1-Visualize/translations/README.zh-cn.md) 1. [介绍聚类](../1-Visualize/translations/README.zh-cn.md)
2. [K-Means 聚类](../2-K-Means/translations/README.zh-cn.md) 2. [K-Means 聚类](../2-K-Means/translations/README.zh-cn.md)
## 致谢 ## 致谢
这些课程由 Jen Looper 在 🎶 上撰写，并由 [Rishit Dagli](https://rishit_dagli) 和 [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan) 进行了有帮助的评审。 这些课程由 Jen Looper 在 🎶 上撰写，并由 [Rishit Dagli](https://rishit_dagli) 和 [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan) 进行了有帮助的评审。

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