Modeling using K-means clustering algorithm - Kumar, A.; Sinha, R.; Bhattacherjee, V.; Verma, Daya Shankar; Singh, S. (2012 1st International Conference on Recent Advances in Information Technology, 2012)
Modeling is an abstract representation of real world process. Predicting the likely behavior from observed behavior would be entirely legitimate if the relationship were found in the data. Two common data mining techniques for finding hidden patterns in data are clustering and classification analyses. Classification is supposed to be supervised learning and clustering is an unsupervised classification with no predefined classes. Clustering tries to group a set of objects and find whether there is some relationship between those objects. In this paper we have used the numerical results generated through the Probability Density Function algorithm as the basis of recommendations in favor of the K-means clustering for weather-related predictions. We propose a model for predicting the probability of the outcome of the Play class as YES or NO through K-means clustering on weather data. The main reason for our choice in favor of K-means clustering algorithm is that it is robust.