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Table Of Contents
Practical Systems Programming in Go
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Time Series Clustering is an unsupervised learning technique where the objective is to partition data (time series in our case) into distinct groups or "clusters" such that sequences within the same group are highly similar to each other while being significantly different from those in other groups. Unlike classification, there are no pre-defined labels; the algorithm must discover the underlying structure of the data on its own, making it invaluable for exploratory data analysis — such as identifying common usage patterns in server logs or categorizing stock movements.
The K-Means Clustering utility organizes your time series into k distinct groups by iteratively refining the center point, or centroid, of each cluster. The process begins by randomly selecting k files to serve as the initial centroids. In the assignment phase, the program uses parallel processing to compare every time series against these centroids, assigning each file...