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Introduction
This MATLAB script implements a two-phase sequence designed to group numerical data based on a specified deviation criteria. The algorithm processes an input dataset and organizes elements into groups where all members within each group fall within a defined deviation range from the group's average value. This approach is particularly valuable for statistical analysis, data clustering, and pattern recognition applications where grouping similar values is essential. The algorithm handles large datasets (up to 10,000 elements in the shared example) and offers a structured output of grouped elements that meet the deviation constraint.
Learning Objectives
Data Grouping by Deviation: To cluster data elements into groups where all members satisfy a maximum deviation threshold from the group average.
Efficient Processing of Large Datasets: To handle substantial input sizes (10,000 elements) through optimized matrix operations.
Two-Phase Processing: To implement an initial grouping phase followed by a refinement phase that ensures all grouped elements meet the deviation criteria.
Validation and Verification: Trying to verify that the sum of input elements equals the sum of output elements after processing.
Performance Measurement: To track computational efficiency through iteration counting and execution time measurement.
Zero Handling: To properly handle zero values by excluding them from average calculations and final groupings.
PDH Eligibility
To request a PDH certificate, don't hesitate to email author with solved quiz.
Disclaimer
The author and publisher of this book have made effort to ensure the accuracy of the information contained herein. However, this book is shared for informational purposes only and is not intended as professional, legal, medical, or financial advice. The reader is responsible for their own actions and decisions. The author shall not be held liable or responsible for any loss, damage, or injury caused, or alleged to be caused, directly or indirectly, by the information contained in this book.
This MATLAB script implements a two-phase sequence designed to group numerical data based on a specified deviation criteria. The algorithm processes an input dataset and organizes elements into groups where all members within each group fall within a defined deviation range from the group's average value. This approach is particularly valuable for statistical analysis, data clustering, and pattern recognition applications where grouping similar values is essential. The algorithm handles large datasets (up to 10,000 elements in the shared example) and offers a structured output of grouped elements that meet the deviation constraint.
Learning Objectives
Data Grouping by Deviation: To cluster data elements into groups where all members satisfy a maximum deviation threshold from the group average.
Efficient Processing of Large Datasets: To handle substantial input sizes (10,000 elements) through optimized matrix operations.
Two-Phase Processing: To implement an initial grouping phase followed by a refinement phase that ensures all grouped elements meet the deviation criteria.
Validation and Verification: Trying to verify that the sum of input elements equals the sum of output elements after processing.
Performance Measurement: To track computational efficiency through iteration counting and execution time measurement.
Zero Handling: To properly handle zero values by excluding them from average calculations and final groupings.
PDH Eligibility
To request a PDH certificate, don't hesitate to email author with solved quiz.
Disclaimer
The author and publisher of this book have made effort to ensure the accuracy of the information contained herein. However, this book is shared for informational purposes only and is not intended as professional, legal, medical, or financial advice. The reader is responsible for their own actions and decisions. The author shall not be held liable or responsible for any loss, damage, or injury caused, or alleged to be caused, directly or indirectly, by the information contained in this book.
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