Identifying the local business trends in cities using data mining techniques

Abstract

With the growing popularity of social media, and several users using them, humongous amount of information is being generated. This information may be 140 words like tweets, posts, and images shared on Facebook or reviews written on Yelp. It would be valuable to both consumers and businesses if the current local business trends followed by the people of different cities can be identified. Local business reviews are available, which when mined can be used to find out the local business trends across cities. With this information, the users can watch out for the prevalent local businesses (yoga, beauty and spas, ballet, restaurant, etc.) in each city, and the businesses can chart their b-plans, accordingly. To accomplish this, the present work uses data mining technique of clustering on benchmark dataset.