Learn Cluster Analysis |Cluster Analysis Tutorial in SPSS |Master in Minutes #tutorial #clustering #segmentation #marketinganalytics
What is Cluster Analysis?
Cluster analysis is a powerful exploratory data analysis technique used to uncover hidden patterns and structures within data. By sorting objects into groups based on similarity, cluster analysis helps in understanding the underlying relationships and associations among data points. In this tutorial, we will delve into the objective of cluster analysis, the methodologies employed, and how to interpret the results effectively.
Objective of Cluster Analysis: The primary objective of cluster analysis is to group similar objects together while maximizing the degree of association within each group and minimizing it between different groups. This process aids in identifying inherent structures within data, revealing meaningful insights, and simplifying complex datasets for further analysis.
Methodologies Used in Cluster Analysis:
Cluster analysis methodologies can be broadly classified into the following categories:
Partitioning Method: Involves partitioning the dataset into a predetermined number of clusters.
Examples include K-means and K-medoids algorithms.
Hierarchical Clustering: Builds a hierarchy of clusters by recursively merging or splitting clusters.
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