A novel Intelligent Hybrid Method for K-Means Algorithm Using Neural Network Solutions

The intelligent hybrid methods are used for improving the performance of K-Means algorithm in this study. This research analyzed different kinds of scenarios of clustering mechanism. In data mining scope, fuzzy methods, evolutionary methods and neural network solutions can improve the performance of this algorithm. The purpose of this study is explained different intelligent methods and is discussed neural network solutions about it. Several neural network solutions existed in literature review. This study focuses on SOM (Self Organization Mapping), ELM (Extreme Learning Machines), deep learning and poly numerals networks. The purpose is evaluating a new hybrid method for K- Mean's algorithm.