Clustering Techniques
Predictive Analytics
| Intermediate
- 10 videos | 35m 24s
- Includes Assessment
- Earns a Badge
The key to meaningful analysis is the ability to choose the right methods that provide the greatest predictive power. Discover how data clustering, such as K-Means, hierarchical, and DBSCAN, is used to combine similar subsets of data.
WHAT YOU WILL LEARN
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recognize characteristics of clustering
identify the different types of clustering
calculate proximity
list key features of K-Means Clustering
recognize key steps for reducing the sum of squared errors in K-Means Clustering -
recognize key steps for the termination of K-Means Clustering iterations
evaluate K-Means Clustering
list key features of Hierarchical Clustering and DBSCAN
recognize key steps in DBSCAN
perform DBSCAN
IN THIS COURSE
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1.Introduction to Clustering4m 1sUP NEXT
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2.Types of Clustering Techniques4m 18s
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3.Proximity Measures for Clustering4m
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4.Overview of K-Means Clustering2m 52s
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5.Minimizing SSE of Data Points4m 42s
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6.K-Means Clustering Termination Procedures1m 59s
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7.Evaluation and Considerations for K-Means Clustering3m 55s
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8.Hierarchical Clustering and DBSCAN Overview4m 18s
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9.DBSCAN Operation2m 16s
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10.DBSCAN Attributes3m 4s
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