Answers.Body: <p>Note: K-means does not assume an interpretation/label of the clusterings - in fact it is an <a href="https://en.wikipedia.org/wiki/Unsupervised_learning" rel="nofollow noreferrer">unsupervised</a> algorithm. The interpretations are a result of <strong>human analysis</strong> <em>after</em> running K-means.</p>
  
  <p>For example, in the case of cats and dogs one would most definitely chose k = 2 - which provides an easy interpretation. However, what would it mean if we set k = 1000. We no longer have a "clean" interpretation of the centroids. </p>
  
  <p>Note: how I keep saying "interpretation." The algorithm simply assigns a data point to a cluster and calls it a day. Humans then look at the results and try to <strong>understand</strong> them with an <strong>interpretation</strong>. </p>
  
  <p>Continuing with the example where k = 2. One could easily interpret "is cat" as "not dog" and "is dog" as "not cat." The idea here is that the data is <strong>unlabeled</strong> beforehand and humans try to fathom the results retrospectively by assigning the resulting clusters with an understandable label.</p>
  
  <p>I hope this clarifies the issue.</p>
  
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Answers.CreationDate: 2019-09-01T05:36:04.327Z
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Answers.Body: <p>The purpose of clustering is to look for the <strong>same characteristics</strong> in the data and group them into clusters. The number of clusters we take is based on how our <strong>clustering algorithm evaluated</strong>.</p>
  
  <p>For example, we use the <strong>Elbow Method</strong> for evaluates. We take optimal number of clusters that distortion start decreasing in a linear fashion. In the picture below, the <strong>optimal number of clusters</strong> for the data is 3.</p>
  
  <p><a href="https://i.sstatic.net/0MEmU.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/0MEmU.png" alt="enter image description here"></a></p>
  
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Answers.UserDisplayName: Iriyanto
Body: <p>K-means tries to find centroid and then clusters around the centroids. But what if we want to cluster based on the complement?</p>
  
  <p>For example, suppose we have a group of animals and we want to cluster Dogs, Cats, (Not Dogs and Not Cats). The 3rd category will not arise from mean clustering.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-08-29T13:58:18.927Z
Comments.Text: generally clustering (as you examplify with k-means) requires a representation. What would *not* refer to in this space?
Comments.UserDisplayName: mshlis
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-09-01T00:04:33.437Z
Comments.Text: Why do you say that the 3rd category will not be arisen from the mean clustering? What is the 3rd category? _Not Dogs_? Btw, what do you mean by "mean clustering"? Do you mean "k-means cluster algorithm"?
Comments.UserDisplayName: nbro
Tags: <machine-learning><unsupervised-learning><clustering>
Title: How can I cluster based on the complementary categories?
UserDisplayName: user3180
ThreadText: # How can I cluster based on the complementary categories?
  
  (Asked by: username_0 on 2019-08-28)
  
  K-means tries to find centroid and then clusters around the centroids. But what if we want to cluster based on the complement?
  
  For example, suppose we have a group of animals and we want to cluster Dogs, Cats, (Not Dogs and Not Cats). The 3rd category will not arise from mean clustering.
  
  **Tags:** machine-learning, unsupervised-learning, clustering
  > **Comments:**
  > **username_1**: generally clustering (as you examplify with k-means) requires a representation. What would *not* refer to in this space?  
  > **username_2**: Why do you say that the 3rd category will not be arisen from the mean clustering? What is the 3rd category? _Not Dogs_? Btw, what do you mean by "mean clustering"? Do you mean "k-means cluster algorithm"?  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_3 on 2019-09-01. Score: 1
  
  Note: K-means does not assume an interpretation/label of the clusterings - in fact it is an [unsupervised](https://en.wikipedia.org/wiki/Unsupervised_learning) algorithm. The interpretations are a result of **human analysis** *after* running K-means.
  
  For example, in the case of cats and dogs one would most definitely chose k = 2 - which provides an easy interpretation. However, what would it mean if we set k = 1000. We no longer have a "clean" interpretation of the centroids.
  
  Note: how I keep saying "interpretation." The algorithm simply assigns a data point to a cluster and calls it a day. Humans then look at the results and try to **understand** them with an **interpretation**.
  
  Continuing with the example where k = 2. One could easily interpret "is cat" as "not dog" and "is dog" as "not cat." The idea here is that the data is **unlabeled** beforehand and humans try to fathom the results retrospectively by assigning the resulting clusters with an understandable label.
  
  I hope this clarifies the issue.
  
  
  ---
  
  ### Answer by username_4 on 2020-04-21. Score: 0
  
  The purpose of clustering is to look for the **same characteristics** in the data and group them into clusters. The number of clusters we take is based on how our **clustering algorithm evaluated**.
  
  For example, we use the **Elbow Method** for evaluates. We take optimal number of clusters that distortion start decreasing in a linear fashion. In the picture below, the **optimal number of clusters** for the data is 3.
  
  [![enter image description here](https://i.sstatic.net/0MEmU.png)](https://i.sstatic.net/0MEmU.png)
  
  
  ---
  
  