klotz: clustering*

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  1. Unlock advanced customer segmentation techniques using LLMs, and improve your clustering models with advanced techniques
  2. Elbow curve and Silhouette plots both are very useful techniques for finding the optimal K for K-means clustering
  3. Comparing Clustering Algorithms
    Following table will give a comparison (based on parameters, scalability and metric) of the clustering algorithms in scikit-learn.

    Sr.No Algorithm Name Parameters Scalability Metric Used
    1 K-Means No. of clusters Very large n_samples The distance between points.
    2 Affinity Propagation Damping It’s not scalable with n_samples Graph Distance
    3 Mean-Shift Bandwidth It’s not scalable with n_samples. The distance between points.
    4 Spectral Clustering No.of clusters Medium level of scalability with n_samples. Small level of scalability with n_clusters. Graph Distance
    5 Hierarchical Clustering Distance threshold or No.of clusters Large n_samples Large n_clusters The distance between points.
    6 DBSCAN Size of neighborhood Very large n_samples and medium n_clusters. Nearest point distance
    7 OPTICS Minimum cluster membership Very large n_samples and large n_clusters. The distance between points.
    8 BIRCH Threshold, Branching factor Large n_samples Large n_clusters The Euclidean distance between points.

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