Identifying Non-pulsar Radiation and Predicting Chess Endgame Result Using ARSkNN

We are currently living in a data age. Due to the expansion of Internet of Things platform, there has been an upsurge in the number of devices connected to the Internet. Every device, ranging from smart sensors and smart phones to systems installed in manufacturing units, hospitals and vehicles is generating data. Such developments have not only escalated the generation of data but also created a need for analysis of raw data to identify patterns. Thus, data mining techniques are being deployed extensively to extract information. The accuracy and effectiveness of data mining techniques in providing better outcomes and cost-effective methods in various domains has already been established. Usually, in supervised learning, distance estimation is used by instance-based learning classifiers like kNN. In this analysis, the regular kNN classifier has been compared with ARSkNN which instead of following the conventional procedure of distance estimation uses the mass estimation approach. ARSkNN has been proved to be commensurate (or superior) to kNN in accuracy and has been found to reduce the computation time drastically on datasets chosen for this analysis.