Performance Analysis of KNN on Different Types of Attributes

Abstract

Data Mining is an inter-disciplinary promising field that focuses on access of information useful for high level decisions and also includes Machine Learning. Data Miners evaluate and filter the data as a result and convert the data into useful information. The useful information is converted into knowledge by performing some techniques. The K-Nearest Neighbor (K-NN) algorithm is an instance based learning method that has been widely used in many pattern classification tasks due to its simplicity, effectiveness and robustness. This paper presents a performance comparison of KNN algorithm in various data sets that includes different types of attributes. The results of this paper are achieved using WEKA tool.