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Data Mining and Machine Learning...

It's all about data ..

 

Data Mining and Machine Learning > Clustering



What is Clustering?
Clustering is the process of grouping similar data points together based on certain features or characteristics.

Why is Clustering Important?
Clustering is important because it helps uncover hidden patterns and structures within data, enabling insights for various applications such as customer segmentation, anomaly detection, and data compression.

What are the Challenges of Clustering?
The challenges of clustering include determining the optimal number of clusters, handling high-dimensional data, dealing with non-linear and non-convex cluster shapes, and addressing sensitivity to initial conditions and noise.

What types of Clustering Algorithms are there?
Clustering algorithms can be categorized into partitioning methods (e.g., K-means), hierarchical methods (e.g., agglomerative clustering), density-based methods (e.g., DBSCAN), and distribution-based methods (e.g., Gaussian mixture models).

What is a very simple Clustering Python example?
Example to show clustering (very simple example, but fully working and complete) - We randomly generate object sizes between 0 and 10 and then use K-means clustering to group them into two clusters based on their size. Finally, we visualize the clusters, with one cluster representing "big" objects and the other representing "small" objects.













 
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