Application Based Categorization of Datasets for Implementing Data Mining Techniques

Clustering, Classification, Prediction, Association, outlier analysis and evolution and deviation analysis are data mining functionalities which are widely used for improving health care, customer segmentation, fraud detection, etc. Variety of datasets are available for implementing these techniques. In this paper, various verified datasets used for implementing data mining techniques, are discussed in detail. All the datasets are described and are categorized based upon their application area. Various dataset repositories such as promise, UCI, kaggle, etc. contain further application based datasets which can be used as per database functionality implemented in research. Experiments are conducted to discuss the usage of these datasets to implement data mining applications. Scopus and Web of science resources are also used in this work to conduct experiments using global analysis, citation count, source-wise and author wise number of documents available, etc. The focus of this work is to conduct application based categorization of datasets and analyzing research trends in data mining domain.