*Rani of Hearts-Reena Kapoor Paristan#55*The Cave Woman - Page 92

Originally posted by: Reenatyzed

so tell me which is ur fav restaurant :v

idk o_O

bundu khan may b... idk o_O

Originally posted by: Reenatyzed

AT corona se darna nhi larna hai😆

haan AT bar bar hath dhoya kro 🤣

ao AT hum prhte hen.. i got exam

idk if i'll ever study here again or not.. bcz my masters is also over after this exam

thank u for being here 🤗

requirements of clustering:

boht sarey ;_;

too much lazy to type..

birch:

It overcomes the two difficulties in agglomerative clustering methods: (1) scalability and (2) the inability to undo what was done in the previous step

decrease the total SSE

Split a cluster: The cluster with the largest SSE is usually chosen, but we could also split the cluster with the largest standard deviation for one particular attribute

Introduce a new cluster centroid: Often the point that is farthest from any cluster center is chosen.

decrease the number of clusters, while trying to minimize the increase in total SSE

Disperse a cluster: This is accomplished by removing the centroid that corresponds to the cluster and reassigning the points to other clusters

Merge two clusters: The clusters with the closest centroids are typically chosen or merge the two clusters that result in the smallest increase in total SSE

k-medoid

Advantages:

1. It is simple to understand and easy to implement.
2. K-Medoid Algorithm is fast and converges in a fixed number of steps.
3. PAM is less sensitive to outliers than other partitioning algorithms

Disadvantages:

1. It is not suitable for clustering non-spherical groups of objects.
2. It may obtain different results for different runs on the same dataset because the first k medoids are chosen randomly.

Advantages of k-means

• Relatively simple to implement.
• Scales to large data sets.
• Guarantees convergence.
• Can warm-start the positions of centroids.
• Easily adapts to new examples.
• Generalizes to clusters of different shapes and sizes, such as elliptical clusters.

Disadvantages of k-means

• Choosing k manually.
• Dependent on initial values.
• Trouble clustering data where clusters are of varying sizes and density.
• Centroids can be dragged by outliers.

advantages of Hierarchical Clustering:

• we do not need to specify the number of clusters required for the algorithm.
• It is easy to implement.
• The dendrogram produced is very useful in understanding the data

disadvantages of Hierarchical Clustering:

• Lack of a Global Objective Function: Agglomerative hierarchical clustering cannot be viewed as globally optimizing an objective function. It uses various criteria to decide locally.
  
• Merging Decisions Are Final: Once a decision is made to merge two clusters, it cannot be undone at a later time.