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clustering

Innocent Iguana answered on February 10, 2023 Popularity 7/10 Helpfulness 5/10

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clustering

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UNSUPERVISED LEARNING process of Dividing data into similar 
groups or segments (clusters). The concept is: similar data tightly group 
together while dissimilar groups show invisible partitions among them.
Process of identifying and dividing similar data into categories
based on similar patterns they share with each other.
eg: How a chimpanzee can identify circular, rectangular shapes and separate them.

Used for
- Exploratory data analysis
- Summary generalization
- Outlier detection / Noise removal
- Finding duplicates
- Pre-processing step 

Different algorithms:
1. Partition based clustering
	- Divides data into non-overlapping subsets of data of similar examples
	- fair-like (kind of same shape) clusters, relatively efficient
    - K-means, K-medians, Fuzzy c-means
2. Hierarchical clustering
	- Produces trees of clusters, good for small dataset
    - Agglomerative, Divisive
3. Density based clustering
	- Produce arbitrary shaped clusters, good to deal with noisy data
    - DBSCAN

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Contributed on Feb 10 2023

Innocent Iguana

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Instances are packet close together inside an availability zone. this enables workload to achieve low-latency network performance necessary for tightly-coupled node-to-node communication typical of HPC application (High Performance Computing).

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Contributed on Feb 21 2023

Pragya Keshap

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clustering

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