Contents
What can K means clustering be used for?
The K-means clustering algorithm is used to find groups which have not been explicitly labeled in the data. This can be used to confirm business assumptions about what types of groups exist or to identify unknown groups in complex data sets.
What are the strengths of K-means algorithm?
One of the biggest advantages of k-means is that it is really easy to implement and—even more important—most of the time you don’t even have to implement it yourself! For most of the common programming languages used in data science an efficient implementation of k-means already exists.
Which is an application of k means clustering?
One of the major application of K means clustering is segmentation of customers to get a better understanding of them which in turn could be used to increase the revenue of the company. Context In today’s competitive world, it is crucial to understand customer behavior and categorize customers based on…
Which is the optimal k value for WCSS?
Plot the curve of WCSS vs the number of clusters K. The location of a bend (knee) in the plot is generally considered as an indicator of the appropriate number of clusters. The optimal K value is found to be 5 using the elbow method. Finally I made a 3D plot to visualize the spending score of the customers with their annual income.
How to calculate the optimal number of clusters?
Next I plotted Within Cluster Sum Of Squares (WCSS) against the the number of clusters (K Value) to figure out the optimal number of clusters value. WCSS measures sum of distances of observations from their cluster centroids which is given by the below formula. where Yi is centroid for observation Xi.
How to initialize a centroid in KDnuggets?
Initialize centroids by first shuffling the dataset and then randomly selecting K data points for the centroids without replacement. Keep iterating until there is no change to the centroids. i.e assignment of data points to clusters isn’t changing.