# Hdbscan vs kmeans

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**k-means**y hierarchical. Esta será la sección práctica, en R. Pero también, este post explorará el punto de intersección de conceptos como reducción de dimensiones, análisis de clustering. 2f' % (dm_m_x1, dm_m_x2)) the Mahalanobis distance measure, c1¡c2 is orthogonal to u¡c1 and

**v**¡c2, for any u 2 X1 and

**v**2 X2 from sklearn import cluster, datasets For distance-based pair trading, we need to normalize the data of the stocks first and then check the distance

**between**them In order to use the Mahalanobis distance to classify a.

**K**

**means**is a bad clustering algo in general. Try DBSCAN /

**HDBSCAN**or others. Also, a lower silhouette score is worse, not better. 1. Reply. Share. Report Save Follow. More posts from the MachineLearning community. 717. Posted by 15 hours ago. Shameless Self Promo [P] The easiest way to process and tag video data - update.

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**K-means**, but the centroid of the cluster is defined to be one of the points in the cluster (the medoid ). K-centers : Similar problem definition as in

**K-means**, but the goal now is to minimize the maximum diameter of the clusters (diameter of a cluster is maximum distance between any two points in the cluster).