K-medoids, K-means

Great Learning and lot of revisions needed to really deep dive and understand the fundamentals.

K-means

• Prone to outliers (Squared Euclidean gives greater weight to more distant points)
• Can't handle categorical data
• Work with Euclidean only

K-Medoids

• Restrict centre to data points
• Centre picked up only from data points
• We use same sum of squares for cost function but distance is not Euclidean distance
• Use your own custom distance functions when involved with numerical and categorical variables
• Example (25 languages, 24 columns, M/F/N - 2 columns) - Compute your own custom distance functions. It is one less because all zero combinations will also be treated as one attribute

Distance measure for numerical variables

• Euclidean based distance
• Correlation based distance
• Mahalanobis distance

Distance measure for category variables

• Matching coef and Jaquard’s coef

Happy Learning!!!