A Concise, Non-Mathematical Beginner's Guide to Principal Component & Cluster Analysis
Using Microsoft Excel
by Kilem L. Gwet, PhD

Unlock the Power of Multivariate Data Analysis

This book explores two distinct yet related multivariate data analysis techniques: Principal Component Analysis (PCA) and Cluster Analysis. Both methods have extensive applications in fields like machine learning and artificial intelligence. PCA enables you to simplify complex, multidimensional problems by reducing them to a few manageable dimensions, often allowing for a visual representation of the data. On the other hand, Cluster Analysis lets you divide a heterogeneous set of units into more homogeneous groups, known as clusters, based on several attributes associated with these units. These techniques are particularly effective when applied to datasets containing numerous units with multiple numeric attributes.

Why read this book?

Principal Component Analysis helps you distill complex, multidimensional problems into manageable dimensions, enabling clearer data visualization and interpretation. Cluster Analysis, on the other hand, allows you to categorize data into meaningful groups based on multiple attributes, revealing hidden patterns within your dataset. Together, these methods offer a powerful toolkit for anyone working with data-heavy environments.

This book offers a non-mathematical account of these techniques and guides you step-by-step through applying them using accessible Excel-based tools. Whether a beginner or an experienced analyst, you'll find practical insights and strategies to enhance your data analysis skills.

What sets this book apart?

You'll learn how to integrate these techniques for more efficient and effective data analysis by exploring both PCA and Cluster Analysis in one cohesive guide. Discover how PCA can optimize the k-Means clustering algorithm, helping you make informed decisions and extract valuable insights from your data.

Rooted in a rich history, with foundations laid by pioneers like Pearson and Hotelling, these techniques have stood the test of time and remain integral to modern data science. Whether you're looking to deepen your understanding or apply these methods to real-world problems, this book is your essential guide to mastering multivariate data analysis.