Writing good code requires close attention to detail and lots of will power and discipline. Good habits are half the battle.

K-Means Algorithm, Influenza Transmission, Cluster Analysis, Urban Characteristics Share and Cite: Ye, S. (2025) Application ...

The k-value at that point is often a good choice. This is called the "elbow" technique. An alternative for clustering mixed categorical and numeric data is to use an old technique called k-prototypes ...

Abstract The k-means algorithm is a popular data clustering technique due to its speed and simplicity. However, it is susceptible to issues such as sensitivity to the chosen seeds, and inaccurate ...

Common clustering techniques include k-means, Gaussian mixture model, density-based and spectral. This article explains how to implement one version of k-means clustering from scratch using the C# ...

The k-means algorithm and adaptive k-means clustering is used to obtain high performance and efficiency in image segmentation.

Various PN-codes can be generated using Linear Feedback Shift Register (LFSR). All the results presented here were tested and simulated via MatLab programs.