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	<title>Unsupervised Learning - Revision history</title>
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	<updated>2026-05-15T12:07:40Z</updated>
	<subtitle>Revision history for this page on the wiki</subtitle>
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		<id>https://qbase.texpertssolutions.com/index.php?title=Unsupervised_Learning&amp;diff=231&amp;oldid=prev</id>
		<title>Thakshashila: /* SEO Keywords */</title>
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		<updated>2025-06-10T06:26:08Z</updated>

		<summary type="html">&lt;p&gt;&lt;span class=&quot;autocomment&quot;&gt;SEO Keywords&lt;/span&gt;&lt;/p&gt;
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				&lt;td colspan=&quot;2&quot; style=&quot;background-color: #fff; color: #202122; text-align: center;&quot;&gt;← Older revision&lt;/td&gt;
				&lt;td colspan=&quot;2&quot; style=&quot;background-color: #fff; color: #202122; text-align: center;&quot;&gt;Revision as of 06:26, 10 June 2025&lt;/td&gt;
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		<author><name>Thakshashila</name></author>
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	<entry>
		<id>https://qbase.texpertssolutions.com/index.php?title=Unsupervised_Learning&amp;diff=195&amp;oldid=prev</id>
		<title>Thakshashila: Created page with &quot;= Unsupervised Learning =  &#039;&#039;&#039;Unsupervised Learning&#039;&#039;&#039; is a type of machine learning where the model learns patterns and structures from unlabeled data without predefined outputs.  == What is Unsupervised Learning? ==  In unsupervised learning, the input data has no associated labels. The goal is to explore the data’s inherent structure, group similar data points, or reduce the data’s dimensionality.  == Common Types of Unsupervised Learning ==  * &#039;&#039;&#039;Clustering:&#039;&#039;&#039; G...&quot;</title>
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		<updated>2025-06-10T06:10:12Z</updated>

		<summary type="html">&lt;p&gt;Created page with &amp;quot;= Unsupervised Learning =  &amp;#039;&amp;#039;&amp;#039;Unsupervised Learning&amp;#039;&amp;#039;&amp;#039; is a type of machine learning where the model learns patterns and structures from unlabeled data without predefined outputs.  == What is Unsupervised Learning? ==  In unsupervised learning, the input data has no associated labels. The goal is to explore the data’s inherent structure, group similar data points, or reduce the data’s dimensionality.  == Common Types of Unsupervised Learning ==  * &amp;#039;&amp;#039;&amp;#039;Clustering:&amp;#039;&amp;#039;&amp;#039; G...&amp;quot;&lt;/p&gt;
&lt;p&gt;&lt;b&gt;New page&lt;/b&gt;&lt;/p&gt;&lt;div&gt;= Unsupervised Learning =&lt;br /&gt;
&lt;br /&gt;
&amp;#039;&amp;#039;&amp;#039;Unsupervised Learning&amp;#039;&amp;#039;&amp;#039; is a type of machine learning where the model learns patterns and structures from unlabeled data without predefined outputs.&lt;br /&gt;
&lt;br /&gt;
== What is Unsupervised Learning? ==&lt;br /&gt;
&lt;br /&gt;
In unsupervised learning, the input data has no associated labels. The goal is to explore the data’s inherent structure, group similar data points, or reduce the data’s dimensionality.&lt;br /&gt;
&lt;br /&gt;
== Common Types of Unsupervised Learning ==&lt;br /&gt;
&lt;br /&gt;
* &amp;#039;&amp;#039;&amp;#039;Clustering:&amp;#039;&amp;#039;&amp;#039; Groups similar data points into clusters.  &lt;br /&gt;
  Example: Customer segmentation.  &lt;br /&gt;
* &amp;#039;&amp;#039;&amp;#039;Dimensionality Reduction:&amp;#039;&amp;#039;&amp;#039; Reduces the number of variables while preserving important information.  &lt;br /&gt;
  Example: Principal Component Analysis (PCA).  &lt;br /&gt;
* &amp;#039;&amp;#039;&amp;#039;Association Rule Learning:&amp;#039;&amp;#039;&amp;#039; Finds interesting relationships or patterns in large datasets.  &lt;br /&gt;
  Example: Market basket analysis.&lt;br /&gt;
&lt;br /&gt;
== How Unsupervised Learning Works ==&lt;br /&gt;
&lt;br /&gt;
1. The model receives unlabeled data.  &lt;br /&gt;
2. It uses similarity or statistical methods to find patterns.  &lt;br /&gt;
3. Results may be clusters, components, or association rules depending on the technique.&lt;br /&gt;
&lt;br /&gt;
== Applications of Unsupervised Learning ==&lt;br /&gt;
&lt;br /&gt;
* Market segmentation.  &lt;br /&gt;
* Anomaly detection.  &lt;br /&gt;
* Data compression.  &lt;br /&gt;
* Recommender systems.  &lt;br /&gt;
* Visualization of complex data.&lt;br /&gt;
&lt;br /&gt;
== Challenges of Unsupervised Learning ==&lt;br /&gt;
&lt;br /&gt;
* No clear measure of accuracy since no labels are available.  &lt;br /&gt;
* Defining meaningful similarity measures.  &lt;br /&gt;
* Determining the number of clusters or components.  &lt;br /&gt;
* Interpreting the discovered patterns.&lt;br /&gt;
&lt;br /&gt;
== Related Pages ==&lt;br /&gt;
&lt;br /&gt;
* [[Clustering]]  &lt;br /&gt;
* [[Dimensionality Reduction]]  &lt;br /&gt;
* [[Association Rule Learning]]  &lt;br /&gt;
* [[K-Means Clustering]]  &lt;br /&gt;
* [[PCA (Principal Component Analysis)]]  &lt;br /&gt;
* [[Evaluation Metrics]]&lt;br /&gt;
&lt;br /&gt;
== SEO Keywords ==&lt;br /&gt;
&lt;br /&gt;
unsupervised learning machine learning, what is unsupervised learning, clustering and unsupervised learning, dimensionality reduction, association rules, unsupervised learning examples, machine learning types&lt;/div&gt;</summary>
		<author><name>Thakshashila</name></author>
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