Exploring Cluster Analysis: Techniques and Applications in Data Science and Market Research

Introduction Cluster analysis is a powerful statistical technique employed in various fields, particularly in data science and market research, to identify patterns and group similar data points. The purpose of this report is to explore the different techniques of cluster analysis, their applications, and the implications of these applications in both data science and market research. By examining the methodologies, the effectiveness of different clustering techniques, and the practical applications in real-world scenarios, this report aims to provide an understanding of how cluster analysis facilitates data-driven decision-making and enhances strategic planning in businesses.

Main Body

Overview of Cluster Analysis Techniques Cluster analysis involves the grouping of a set of objects in such a way that objects in the same group (or cluster) are more similar to each other than to those in other groups. Several techniques have been developed over the years, each with its advantages and drawbacks. The most commonly used clustering methods include: 1.

K-Means Clustering

: This popular method partitions data into K distinct clusters based on feature similarity. It works by initializing K centroids and iteratively