IDENTIFICATION OF EXPLICIT SMARTPHONE FEATURES USING APRIORI ALGORITHM
Smartphone plays a vital role in our daily life which enrich with the tools of Knowledge Data Discovery (KDD) and data mining as a mobile devices. This assist in acquiring and derive better contextual data about users who utilizing their mobile phones. In several organization of e-commerce business have the ability to acquire and store the data using smart phones suppliers, customers and their business details along with feedback. In addition, there are various beneficial vision of marketing are concealed within those huge data. Hence, the process of data mining has utilized to search and analyze the data for identifying the potential information whereas data mining has several computational patterns, models and algorithms to analyze the customer feedback of the smartphone and it feature. This study has selected Apriori algorithm which is one of the standard algorithm for Association Rule Mining (ARM) that can used to mine frequent item sets and its associate rules. Several mining algorithms are available based on associative rule with their mutation has been proposed in the Apriori algorithm but default algorithm is generate more time consumption and less efficiency. Hence, this paper proposed an enhanced apriori algorithm to prune the subset and identify the better frequent item set which identify the better selection for the smartphone that get explicit. The Google Form input survey from students and staff of the Anna University has provide the expectation of smart phone features based on latest trend which assist the retailer and customer to be aware of purchasing by analyzing the previous data. This proposal is done with minimum support threshold that detached the dissimilar items and accomplished N-frequent item sets.