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Data Science #14 - The original k-means algorithm paper review (1957)
At the 14th episode we go over the Stuart Lloyd's 1957 paper, "Least Squares Quantization in PCM," (which was published only at 1982)
The k-means algorithm can be traced back to this paper.
Loyd introduces an approach to quantization in pulse-code modulation (PCM). Which is like a 1-D k means clustering.
Lloyd discusses how quantization intervals and corresponding quantum values should be adjusted based on signal amplitude distributions to minimize noise, improving efficiency in PCM systems.
He derives an optimization framework that minimizes quantization noise under finite quantization schemes.
Lloyd’s algorithm bears significant resemblance to the k-means clustering algorithm, both seeking to minimize a sum of squared errors.
In Lloyd's method, the quantization process is analogous to assigning data points (signal amplitudes) to clusters (quantization intervals) based on proximity to centroids (quantum values), with the centroids updated iteratively based on the mean of the assigned points.
This iterative process of recalculating quantization values mirrors k-means’ recalculation of cluster centroids. While Lloyd’s work focuses on signal processing in telecommunications, its underlying principles of optimizing quantization have clear parallels with the k-means method used in clustering tasks in data science.
The paper's influence on modern data science is profound. Lloyd's algorithm not only laid the groundwork for k-means but also provided a fundamental understanding of quantization error minimization, critical in fields such as machine learning, image compression, and signal processing.
The algorithm's simplicity, combined with its iterative nature, has led to its wide adoption in various data science applications. Lloyd's work remains a cornerstone in both the theory of clustering algorithms and practical applications in signal and data compression technologies.
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By Mike E
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33 ratings
At the 14th episode we go over the Stuart Lloyd's 1957 paper, "Least Squares Quantization in PCM," (which was published only at 1982)
The k-means algorithm can be traced back to this paper.
Loyd introduces an approach to quantization in pulse-code modulation (PCM). Which is like a 1-D k means clustering.
Lloyd discusses how quantization intervals and corresponding quantum values should be adjusted based on signal amplitude distributions to minimize noise, improving efficiency in PCM systems.
He derives an optimization framework that minimizes quantization noise under finite quantization schemes.
Lloyd’s algorithm bears significant resemblance to the k-means clustering algorithm, both seeking to minimize a sum of squared errors.
In Lloyd's method, the quantization process is analogous to assigning data points (signal amplitudes) to clusters (quantization intervals) based on proximity to centroids (quantum values), with the centroids updated iteratively based on the mean of the assigned points.
This iterative process of recalculating quantization values mirrors k-means’ recalculation of cluster centroids. While Lloyd’s work focuses on signal processing in telecommunications, its underlying principles of optimizing quantization have clear parallels with the k-means method used in clustering tasks in data science.
The paper's influence on modern data science is profound. Lloyd's algorithm not only laid the groundwork for k-means but also provided a fundamental understanding of quantization error minimization, critical in fields such as machine learning, image compression, and signal processing.
The algorithm's simplicity, combined with its iterative nature, has led to its wide adoption in various data science applications. Lloyd's work remains a cornerstone in both the theory of clustering algorithms and practical applications in signal and data compression technologies.
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