Data Mining and Data Warehousing Part 20 | CLARANS Algorithm in Data Mining | Data Analytics | Data Science | Machine Learning | by Prem Sir | PremnArya

Data Mining and Data Warehousing Part 20 | CLARANS Algorithm in Data Mining | Data Analytics | Data Science | Machine Learning | by Prem Sir | PremnArya

About the video:
This video explains the following contents in detail with examples and diagrams. This topic also related to Machine Learning, Data Science, Data Analytics, Big Data, etc.
1. CLARANS Algorithm
2. CLARANS Algorithm Functioning
3. CLARANS Algorithm Steps
4. Comparison between CLARA & CLARANS
5. Advantages of CLARANS Algorithm
6. Limitations or drawback of CLARA Algorithm
7. PAM Algorithm or Partitioning Around Medoids
8. Partitioning Technique or methods
9. Unsupervised methods or technique
10. Clustering Large Applications based upon RANdomized Search

This video also explained the limitations or drawback of the CLARA algorithm:
1. The best k medoids may not be selected during the sampling process, in this case, CLARA will never find the best clustering.
2. If the sampling is biased or partial, we cannot find good quality clusters.
3. Trade-off efficiency.

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