Enhanced expectation–maximization clustering through Gaussian mixture models

Abstract

Clustering is the most important task in data mining. For the intelligent clustering is also the part of the machine learning. Various existing systems are introduced for better clustering. In the past decade so many existing clustering algorithms are introduced to perform better results. These algorithms work on extracting the patterns from the unsupervised decision tree. Binary cuckoo search based decision tree is adopted with Expectation–Maximization (EM) Clustering through Gaussian Mixture Models (GMM) to improve performance of the clustering. Here we are using numerical data set, mushroom and MIST dataset to extract patterns using clustering. The performance will be estimated in terms of various measures like sensitivity, specificity, and accuracy.