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2025-06-10T06:20:52Z

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	clustering machine learning, what is clustering, clustering algorithms, types of clustering, unsupervised learning clustering, K-means clustering explained, DBSCAN clustering, clustering applications		clustering machine learning, what is clustering, clustering algorithms, types of clustering, unsupervised learning clustering, K-means clustering explained, DBSCAN clustering, clustering applications
			[[Category:Artificial Intelligence]]

Thakshashila: Created page with "= Clustering = '''Clustering''' is an unsupervised machine learning technique that groups data points into clusters such that points in the same cluster are more similar to each other than to those in other clusters. == What is Clustering? == Unlike supervised learning, clustering does not use labeled data. The goal is to find natural groupings or patterns within the data based on similarity or distance measures. == Types of Clustering == * '''Partitioning Methods:'..."

2025-06-10T06:08:14Z

Created page with "= Clustering = '''Clustering''' is an unsupervised machine learning technique that groups data points into clusters such that points in the same cluster are more similar to each other than to those in other clusters. == What is Clustering? == Unlike supervised learning, clustering does not use labeled data. The goal is to find natural groupings or patterns within the data based on similarity or distance measures. == Types of Clustering == * '''Partitioning Methods:'..."

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= Clustering =

'''Clustering''' is an unsupervised machine learning technique that groups data points into clusters such that points in the same cluster are more similar to each other than to those in other clusters.

== What is Clustering? ==

Unlike supervised learning, clustering does not use labeled data. The goal is to find natural groupings or patterns within the data based on similarity or distance measures.

== Types of Clustering ==

* '''Partitioning Methods:''' Divide data into a set number of clusters.
Example: K-Means clustering.
* '''Hierarchical Clustering:''' Builds a tree of clusters by merging or splitting them.
* '''Density-Based Clustering:''' Groups points based on density of data points in regions.
Example: DBSCAN.
* '''Model-Based Clustering:''' Assumes data is generated by a mixture of underlying probability distributions.

== How Clustering Works ==

1. Select the number of clusters or let the algorithm determine it.
2. Calculate similarity/distance between data points (e.g., Euclidean distance).
3. Assign points to clusters based on similarity criteria.
4. Update clusters iteratively until stable.

== Popular Clustering Algorithms ==

* K-Means
* Hierarchical Agglomerative Clustering
* DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
* Gaussian Mixture Models (GMM)

== Applications of Clustering ==

* Customer segmentation in marketing
* Image segmentation in computer vision
* Anomaly detection
* Document or text grouping
* Bioinformatics for gene expression analysis

== Challenges in Clustering ==

* Choosing the right number of clusters.
* Handling noisy data and outliers.
* Defining appropriate similarity measures.
* Computational complexity for large datasets.

== Related Pages ==

* [[Unsupervised Learning]]
* [[Classification]]
* [[K-Means Algorithm]]
* [[DBSCAN]]
* [[Dimensionality Reduction]]
* [[Evaluation Metrics]]

== SEO Keywords ==

clustering machine learning, what is clustering, clustering algorithms, types of clustering, unsupervised learning clustering, K-means clustering explained, DBSCAN clustering, clustering applications

Clustering - Revision history

Thakshashila: /* SEO Keywords */