Analysis of online news popularity and bank marketing using ARSkNN

Data mining is a process of evaluating practice of examining large preexisting databases in order to generate new information. The amount of data has been growing at an enormous rate ever since the development of computers and information technology. Many methods and algorithms have been developed in the last half-century to evaluate data and extract useful information to help develop faster. Due to the wide variety of algorithms and different approaches to evaluate data, several algorithms are compared. The performance of any algorithm on a particular dataset cannot be predicted without evaluating it with the same constraints and parameters. The following paper is a comparison between the trivial kNN algorithm and the newly proposed ARSkNN algorithm on classifying two datasets and subsequently evaluating their performance on average accuracy percentage and average runtime parameters.