Enhanced Expectation–Maximization Clustering through Gaussian Mixture Models
S. Nagarjuna Reddy1, S. Sai Satyanarayana Reddy2, M. Babu Reddy3
1S. Nagarjuna Reddy, Research Scholor, Department of Computer Science Engineering, JNTUK, Kakinada, India.
2S. Sai Satyanarayana Redy, Department of Computer Science Engineering, Vardhaman College of engineering, Hyderabad, India.
3M. Babu Reddy, Department of Computer Science Engineering, Krishna University, Machilipatnam, India.
Manuscript received on 29 May 2019 | Revised Manuscript received on 05 June 2019 | Manuscript published on 30 June 2019 | PP: 1818-1822 | Volume-8 Issue-8, June 2019 | Retrieval Number: H6848058719/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: EM-GMM, K-Means, Mushroom, MIST
Scope of the Article: Clustering