Published Paper Details:

Data Mining (referred as extracting knowledge from data) is the process of discovering patterns, associations, and links in the huge stack of data which is based on the analysis done through different perspectives. There are many disciplines which are found under data mining some of them are clustering analysis, regression analysis, classification analysis etc. To analyze the data, we need some sort of software which can be used to perform the required analysis on data set. In this paper, we are using WEKA library (.jar file) for data mining and implementing the outlier detection algorithm to detect the outliers in the data set. Outliers are the points in our data set which is distinct from the remaining data set and including of these points may result in inappropriate outcomes. Outliers are the observation that deviates so much from other observation as to arouse suspicion that it was generated by a different mechanism. In this paper, we are studying various data mining techniques to find outliers in our data set and also finding the best suited algorithm for different sample survey data of India (comprises of Inflation rate, Demonetization, UIDAI, and Pan Card) that will help us to find the set of outliers for our data set.