Data Mining for Staff Selection System using Weka

Data mining, the extraction of hidden forecasting statistics from large databases is a strong technology with great expectation used in various Enterprise applications including retail sales, e-commerce, remote sensing, bioinformatics, banking, business analytics, latest mobile applications, ERP, SAP etc. Staff Selection is an essential element for the progress of country. Mining in selection environment is called Selective Data Mining. Staff Selection data mining is concerned with developing new methods to discover knowledge from Staff database. In order to examine student swing & reaction towards education an attempt to study the present behavioral pattern of staff in a cross section is a must. This paper surveys an application of data mining in Staff Selection system and also present analysis using WEKA (Waikato Environment for Knowledge Analysis) tool. Weka is a collection of machine learning algorithms for data mining tasks. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes. As we know large amount of data is stored in database, so in order to retrieve required data and to find the secret relationship, different data mining techniques are developed and used worldwide. There are variety of different data mining task e.g. classification, clustering, outliers detection, association rule, prediction.