Optimizing K-means is still an active area of research for purpose of clustering. Recent developments in Cloud Computing have resulted in emergence of Big Data Analytics. There is a fresh need of simple, fast yet accurate algorithm for clustering huge amount of data. This paper proposes optimization of K-means through reduction of the points which are considered for re-clustering in each iteration. The work is generalization of earlier work by Poteras et al who proposed this idea. The suggested scheme has an improved average runtime. The cost per iteration reduces as number of iterations grow which makes the proposal very scalable.

Text Classification is done mainly through classifiers proposed over the years, Naïve Bayes and Maximum Entropy being the most popular of all. However, the individual classifiers show limited applicability according to their respective domains and scopes. Recent research works evaluated that the combination of classifiers when used for classification showed better performance than the individual ones. This work introduces a modified Maximum Entropy-based classifier. Maximum Entropy classifiers provide a great deal of flexibility for parameter definitions and follow assumptions closer to real world scenario. This classifier is then combined with a Naïve Bayes classifier. Naïve Bayes Classification is a very simple and fast technique. The assumption model is opposite to that of Maximum Entropy. The combination of classifiers is done through operators that linearly combine the results of two classifiers to predict class of documents in query. Proper validation of the 7 proposed modifications (4 modifications of Maximum Entropy, 3 combined classifiers) are demonstrated through implementation and experimenting on real life datasets.