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 clusteringIdentify the different types of clusteringCalculate proximityList key features of k-means clusteringRecognize key steps for reducing the sum of squared errors in k-means clustering
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Recognize key steps for the termination of k-means clustering iterationsEvaluate k-means clusteringList key features of hierarchical clustering and dbscanRecognize key steps in dbscanPerform dbscan
IN THIS COURSE
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4m 1sUpon completion of this video, you will be able to recognize characteristics of clusters. FREE ACCESS
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4m 18sIn this video, you will learn about the different types of clustering. FREE ACCESS
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4mIn this video, you will calculate proximity. FREE ACCESS
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2m 52sUpon completion of this video, you will be able to list key features of K-Means Clustering. FREE ACCESS
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4m 42sUpon completion of this video, you will be able to recognize key steps for reducing the sum of squared errors in K-Means Clustering. FREE ACCESS
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1m 59sAfter completing this video, you will be able to recognize key steps for terminating K-Means Clustering iterations. FREE ACCESS
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3m 55sDuring this video, you will learn how to evaluate K-Means Clustering. FREE ACCESS
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4m 18sAfter completing this video, you will be able to list key features of Hierarchical Clustering and DBSCAN. FREE ACCESS
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2m 16sAfter completing this video, you will be able to recognize key steps in the DBSCAN algorithm. FREE ACCESS
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3m 4sIn this video, you will learn how to perform DBSCAN. FREE ACCESS
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