<p>From reading paper of <a href="https://www.tandfonline.com/doi/abs/10.1207/s15427579jpfm0601_2" rel="nofollow noreferrer">Clark-Murphy, 2010</a>, I saw the authors used two methods: &quot;<strong>cluster analysis</strong>&quot;(CA) and &quot;<strong>discriminant analysis</strong>&quot; (DA)</p>
<p>I found a great source of explanation <a href="https://www.researchgate.net/post/What_are_the_similarities_and_differences_between_cluster_analysis_and_discriminant_analysis" rel="nofollow noreferrer">here</a>.
Essentially speaking, I understand the cluster analysis quite clear that:</p>
<blockquote>
<p>Cluster analysis is somewhat exploratory. It takes a data set and
looks for the &quot;best&quot; cluster solution or grouping of the people based
on their data. Essentially you are trying to find the &quot;true&quot; grouping
pattern. Cluster analysis tries to maximize in-group homogeneity and
maximize between-group heterogeneity.</p>
</blockquote>
<p>However, in this link, I found a lot of discussion about discriminant analysis but I still not yet got the idea. Is there any intuitive explanation for that?</p>
<p>Thanks in advance.</p>

CC BY-SA 4.0
2021-11-08T08:44:43.630Z
Hi @Louise. Overall, cluster analysis (CA) and linear discriminant analysis (LDA) are dimensionality reduction methods. CA methods such as k-means and k-medoids are considered a type of 'unsupervised learning.' Here, these methods are unsupervised because they learn to classify data unconditional of a dependent variable. LDA by comparison is a type of supervised learning, which classifies subsets of data conditional upon a dependent variable. Alongside this, you will find that these methods are typically differentiated by the type of data you have available (whether continuous or categorical)
EB3112
CC BY-SA 4.0
2021-11-08T09:25:27.963Z
@EB3112 a geat explanation, thanks a heap, it is more clear to me now.
Phil Nguyen
<nonexpert>
What is the difference between "cluster analysis" and "discriminant analysis"
Phil Nguyen
# What is the difference between "cluster analysis" and "discriminant analysis"

(Asked by: username_0 on 2021-11-08)

From reading paper of [Clark-Murphy, 2010](https://www.tandfonline.com/doi/abs/10.1207/s15427579jpfm0601_2), I saw the authors used two methods: "**cluster analysis**"(CA) and "**discriminant analysis**" (DA)

I found a great source of explanation [here](https://www.researchgate.net/post/What_are_the_similarities_and_differences_between_cluster_analysis_and_discriminant_analysis).
Essentially speaking, I understand the cluster analysis quite clear that:

> Cluster analysis is somewhat exploratory. It takes a data set and
> looks for the "best" cluster solution or grouping of the people based
> on their data. Essentially you are trying to find the "true" grouping
> pattern. Cluster analysis tries to maximize in-group homogeneity and
> maximize between-group heterogeneity.

However, in this link, I found a lot of discussion about discriminant analysis but I still not yet got the idea. Is there any intuitive explanation for that?

Thanks in advance.

**Tags:** nonexpert

> **Comments:**
> **username_1**: Hi @Louise. Overall, cluster analysis (CA) and linear discriminant analysis (LDA) are dimensionality reduction methods. CA methods such as k-means and k-medoids are considered a type of 'unsupervised learning.' Here, these methods are unsupervised because they learn to classify data unconditional of a dependent variable. LDA by comparison is a type of supervised learning, which classifies subsets of data conditional upon a dependent variable. Alongside this, you will find that these methods are typically differentiated by the type of data you have available (whether continuous or categorical)  
> **username_0**: @username_1 a geat explanation, thanks a heap, it is more clear to me now.  

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## 0 answers

