A Comparison Study on Performance Analysis of Data Mining Algorithms in Classification of Local Area News Dataset using WEKA Tool

Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD), [1] a field at the intersection of computer science and statistics, is the process that attempts to discover patterns in large data sets. It utilizes methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. It is commonly used in marketing, surveillance, fraud detection, scientific discovery and now gaining wide way in social networking. Anything and everything on the Internet is fair game for extreme data mining practices. Social media covers all aspects of the social side of the internet that allow us to get contact and carve up information with others as well as intermingle with any number of people in any place in the world. This paper uses the dataset “Local News Survey” from Pew Research Center. The focus of the research is towards exploration on impact of the internet on Local News activities using Data Mining Techniques. The original dataset contains 102 attributes which is very large and hence the essential attributes required for the analysis are selected by feature reduction method. The selected attributes were applied to Data Mining Classification Algorithms such as RndTree, ID3, K-NN, C4.5 and CS-MC4. The Error rates of various classification Algorithms were compared to bring out the best and effective Algorithm suitable for this dataset.