Learning path
A four-step route through the current algorithm work
Track progress locally as you move from backtracking basics to bitwise optimization, then into the Iris K-means bundle.
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1
Backtracking
Eight queens with classic backtracking
Start with state representation, conflict checks, recursive search, and the full C/Python solving flow.
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2
Optimization
Bitwise optimization for the eight queens problem
Move from array-based checks to compact bit masks so the same search becomes faster and easier to reason about.
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3
Clustering
K-means clustering on the Iris dataset in C
Study standardization, K-means++ initialization, restart selection, SSE, and the final clustering result.
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4
Materials
K-means companion downloads
Download the dataset, C source file, flowchart, visualization, and zip archive used by the article.
