Course details

Clustering Techniques

Clustering Techniques


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



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