Clustering Techniques

Overview/Description
Target Audience
Prerequisites
Expected Duration
Lesson Objectives
Course Number
Expertise Level

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Overview/Description
The key to meaningful analysis is the ability to choose the right methods that provide the greatest predictive power. Explore how data clustering, such as K-Means, hierarchical, and DBSCAN, is used to combine similar subsets of data.

Target Audience
All individuals who are new to predictive analytics and wish to use it to optimize their business performance; business leaders; analysts; marketing, sales, software, and IT professionals who want to add predictive analytics to their skill set; and decision makers of any kind

Prerequisites
None

Expected Duration (hours)
0.7

Lesson Objectives

Clustering Techniques

start the course

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

identify key attributes for performing DBSCAN

Course Number:
df_prma_a11_it_enus

Expertise Level
Intermediate