Abstract: The data mining is the vital point of data combination for business intelligence. Now a day, there has been emerging trends in database to discover useful patterns and/or correlations among attributes called data mining. Here in this research presents the data mining techniques like classification, clustering and associations analysis which is include algorithms of decision tree (like C4.5), rule set classifier, kNN and Naive Bayes, clustering algorithms (like k-Means and EM) machine learning (like SVM), association analysis (like Apriori). These algorithms are applied on a data warehouse for extracting useful information from a big database. All algorithms contain their description, impact and review of algorithms. Here, it is also show the comparison between the classifiers by accuracy which shows ruleset classifier have higher accuracy when implement in MATLAB Software using EXCEL datasheet. These algorithms useful to find out the discrimination of employee, employee performance of industries like banking, insurance, medical, information technology sector, etc. and also age and sex discrimination.
P. Baskaran and K. Arulanandam, 2015. Modern Boost Decision Tree Algorithm: A Novel and Effective Discrimination Prevention Technique Using Data Mining Technique. Research Journal of Applied Sciences, 10: 550-554.