Implementation of Data Mining Algorithms using R

Data mining is an inter disciplinary field and it finds application everywhere. To solve many different day to life problems, the algorithms could be made use. Since R studio is more comfortable for researcher across the globe, most widely used data mining algorithms for different cases studies are implemented in this paper by using R programming language. Could be implemented with help of R programming. The advanced sensing and computing technologies have enabled the collection of large amount of complex data. Data mining techniques can be used to discover useful patterns that in turn can be used for classifying new data or other purpose. The algorithm for processing large set of data is scalable. Algorithm for processing data with changing pattern must be capable of incrementally learning and updating data patterns as new data become available. Still data mining algorithm such as decision tree support the incremental learning of data with mixed data types, the user is not satisfied with scalability of these algorithms in handling large amount of data. The following algorithms were implemented using R studio with complex data set. There are four algorithms in the project- 1) Clustering Algorithm 2) Classification Algorithm 3) Apriori Algorithm 4) Decision Tree Algorithm. It is concluded that R studio produced most efficient result for implementing the above said algorithms. Citation: V. Neethidevan. "Implementation of Data Mining Algorithms using R." Global Research and Development Journal For Engineering 4.7 (2019): 4 - 10.