K+ Means : An Enhancement Over K-Means Clustering Algorithm
This addresses the challenge of selecting K in clustering for data analysis, but appears incremental as it builds directly on the standard K-means method.
The paper tackles the problem of determining the optimal number of clusters K in K-means clustering by proposing the K+ Means algorithm as an enhancement, but no concrete results or numbers are provided in the abstract.
K-means (MacQueen, 1967) [1] is one of the simplest unsupervised learning algorithms that solve the well-known clustering problem. The procedure follows a simple and easy way to classify a given data set to a predefined, say K number of clusters. Determination of K is a difficult job and it is not known that which value of K can partition the objects as per our intuition. To overcome this problem we proposed K+ Means algorithm. This algorithm is an enhancement over K-Means algorithm.