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Data Mining and Data Warehousing Part 20 | CLARANS Algorithm in Data Mining | Data Analytics | Data Science | Machine Learning | by Prem Sir | PremnArya
Description
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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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.
----------------------------------------------------------------------------------
►Follow us on our social media links for regular updates:
Facebook Page: https://www.facebook.com/premnarya10
Twitter: https://twitter.com/premnarya10
Instagram: https://www.instagram.com/premnarya10/
Keywords & Tags
#Premn Arya
#PremnArya
#tutorial
#english
#ugc net
#computer science
#Bhopal
#hindi
#Data Mining and Data Warehousing Part 20
#machine learning
#data mining
#data analytics
#clusterin technique
#data science
#manhattan distance
#k medoid algorithm
#k medoid algorithm in hindi
#k medoid in data mining
#CLARANS Algorithm in data mining
#by prem
#by prem sir
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#clarans algorithm
#pam algorithm
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#pam
#k medoids
#clara algorithm
#clarans clustering algorithm
#artificial intelligence
#database management system
#software modeling and designing
#software engineering and project planning
#data mining and warehouse
#mobile communication
#mobile computing
#computer networks
#high performance computing
#parallel computing
#operating system
#software programming spos
#web technology
#internet of things
#design and analysis of algorithm
#deep learning
#neural network
#GATE
#UGC NET
#Exams
#GATE 2020
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