What type of data is good for clustering?

K-medoids is the discrete version of the K-means algorithm. Other kinds of partition-based clustering algorithms are CLARA, PAM, and CLARANS. The partition-based clustering algorithms are best used with categorical data — for example, grouping the data based on gender, age group, or education level.

What kind of data for K-means clustering?

K-means clustering is a type of unsupervised learning, which is used when you have unlabeled data (i.e., data without defined categories or groups). The goal of this algorithm is to find groups in the data, with the number of groups represented by the variable K.

Is the K-Means algorithm suitable for handling large datasets?

k-means is useless for “big data” K-means cannot be used on such data. k-means only works on low-dimensional, continuous numeric, dense data.

What is clustering good for?

Clustering is an unsupervised machine learning method of identifying and grouping similar data points in larger datasets without concern for the specific outcome. Clustering (sometimes called cluster analysis) is usually used to classify data into structures that are more easily understood and manipulated.

How do you interpret K-means clustering?

It calculates the sum of the square of the points and calculates the average distance. When the value of k is 1, the within-cluster sum of the square will be high. As the value of k increases, the within-cluster sum of square value will decrease.

Is K means fast?

The k-means algorithm is probably the most widely used clustering heuristic, and has the reputation of being fast.

How can K means clustering be improved?

K-means clustering algorithm can be significantly improved by using a better initialization technique, and by repeating (re-starting) the algorithm. When the data has overlapping clusters, k-means can improve the results of the initialization technique.