Preprocessing Using Attribute Selection in Data Stream Mining

Data stream mining has enormous, increasing, dynamic set of data with the time field registered while entering the information in online. It includes numerous attributes which ease the communication and ordering process between the customer and commercial centre. The basic principle in mining is to analyze the data in variant perspectives. The need for exploring such data into useful information in the concept of streaming in online and offline grow the challenge into a major thing. Data collection and preprocessing are the essential and earlier stage in mining. The method used in preprocessing enhances and ease the process unless it will lead to a difficult mining while gaining knowledge about the data. Integrating and transforming the data into an understandable format needs efficient preprocessing tools and procedures. This paper explores the preprocessing methods by applying Attribute Selection in WEKA tool that aids in a simplified and structured set of information. The proposed Attribute selection method removes the irrelevant attribute by using Cfs Subset evaluator with Greedy search method. Finally the size of the file before and after preprocessing, number of attribute elevated through this method is listed as the performance evaluation.