Answers.Body: <p>One problem with clustering algorithms is that they will typically find you a solution, ie they will split your data set into clusters, but it will find you a structure even if there isn't one. Your data looks like it could consist of about 5 to 7 clusters, but it could equally well just be 2 or only 1.</p>
  
  <p>What you need to do after the clustering is to assess the quality of the result. I recommend having a look at <em><a href="https://onlinelibrary.wiley.com/doi/book/10.1002/9780470316801" rel="nofollow noreferrer">Finding Groups in Data</a></em> by Kaufman &amp; Rousseeuw. They discuss various clustering algorithms and also a procedure that works out how cohesive your clusters are. Though it is 30 years old, it is an excellent book on the topic.</p>
  
  <p>You also have the issue of choosing a value for <em>k</em> in your clustering: I usually start with two, and increase it from there; at each step I compute the cohesion of the result using their method, until I get the best score. This is an objective way of finding a good value for <em>k</em> and usually a reasonable clustering result.</p>
  
  <p>The ultimate test, of course, is then if looking at the result makes sense to you. No cluster algorithm can do that for you.</p>
  
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Answers.Text: How can i answer this question that if this dataset is suitable for k-means clustering task or not? considering what measures?
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Answers.Text: There is a cohesion measure described in Kaufman & Rousseeuw. IIRC you calculate for each element the distant to its own cluster centre and the ones of the other clusters; if it's closest to its own, then it's a positive score. But it's been a while since I last did this.
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Answers.Text: Okay but clustering is the next step, i just need to answer by looking at the dataset and it's statistical measures that is it suitable for k-means clustering task or not? so i am confused how can i answer this?
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Body: <p>I am working with the K-means clustering algorithm for unsupervised learning. </p>
  
  <p>Is the following dataset suitable for the k-means clustering task or not? Why or why not? The dataset has only two features.</p>
  
  <p><a href="https://i.sstatic.net/gnc2O.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/gnc2O.png" alt="enter image description here"></a></p>
  
  <p><a href="https://i.sstatic.net/pjzph.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/pjzph.png" alt="enter image description here"></a></p>
  
Tags: <datasets><unsupervised-learning><data-science><clustering><k-means>
Title: Is this dataset with only two features suitable for clustering with k-means?
UserDisplayName: Debugger
ThreadText: # Is this dataset with only two features suitable for clustering with k-means?
  
  (Asked by: username_0 on 2020-05-17)
  
  I am working with the K-means clustering algorithm for unsupervised learning.
  
  Is the following dataset suitable for the k-means clustering task or not? Why or why not? The dataset has only two features.
  
  [![enter image description here](https://i.sstatic.net/gnc2O.png)](https://i.sstatic.net/gnc2O.png)
  
  [![enter image description here](https://i.sstatic.net/pjzph.png)](https://i.sstatic.net/pjzph.png)
  
  **Tags:** datasets, unsupervised-learning, data-science, clustering, k-means
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2020-05-18. Score: 2
  
  One problem with clustering algorithms is that they will typically find you a solution, ie they will split your data set into clusters, but it will find you a structure even if there isn't one. Your data looks like it could consist of about 5 to 7 clusters, but it could equally well just be 2 or only 1.
  
  What you need to do after the clustering is to assess the quality of the result. I recommend having a look at *[Finding Groups in Data](https://onlinelibrary.wiley.com/doi/book/10.1002/9780470316801)* by Kaufman & Rousseeuw. They discuss various clustering algorithms and also a procedure that works out how cohesive your clusters are. Though it is 30 years old, it is an excellent book on the topic.
  
  You also have the issue of choosing a value for *k* in your clustering: I usually start with two, and increase it from there; at each step I compute the cohesion of the result using their method, until I get the best score. This is an objective way of finding a good value for *k* and usually a reasonable clustering result.
  
  The ultimate test, of course, is then if looking at the result makes sense to you. No cluster algorithm can do that for you.
  
  > **Comments:**
  > **username_0**: How can i answer this question that if this dataset is suitable for k-means clustering task or not? considering what measures?  
  > **username_1**: There is a cohesion measure described in Kaufman & Rousseeuw. IIRC you calculate for each element the distant to its own cluster centre and the ones of the other clusters; if it's closest to its own, then it's a positive score. But it's been a while since I last did this.  
  > **username_0**: Okay but clustering is the next step, i just need to answer by looking at the dataset and it's statistical measures that is it suitable for k-means clustering task or not? so i am confused how can i answer this?  
  
  ---
  
  