International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 04 | Apr 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 3338 Online Sequential Behaviour Analysis using Apriori Algorithm Gaurav Vishwakarma 1 , Shradhey Parte 2 , Heem Joshi 3 , Jay Patel 4 , Pravin Jangid 5 1,2,3,4 Dept. of Computer Engineering, Shree L.R. Tiwari College of Engineering Mira Road (East), Thane- 401107, Maharashtra, INDIA 5 Assistant Professor, Dept. of Computer Engineering, Shree L.R. Tiwari College of Engineering Mira Road (East), Thane- 401107, Maharashtra, INDIA ---------------------------------------------------------------------***---------------------------------------------------------------------- Abstract – Nowadays a lot of data is available in sequential format. With the emergence of data mining and its application the business sector has been benefited in the form of extraction and prediction algorithms. This has helped to mine such sequential data to form behavioural pattern of such data and make predictions. Recommendation System is one such tool that has been used by almost every E-commerce site. This project explores scope of frequent item set based recommendation by implementing Apriori algorithm which is mainly used to find frequently purchased items/products. The key idea behind this recommendation is that any item set that occurs frequently together must have each item (or any subset) occur at least as frequently. Key Words: Recommendation System, Apriori Algorithm, Association Rule, Frequent Item set. 1. INTRODUCTION Recommendation systems have become extremely common in recent years. In definition, goal of a Recommender System is to generate relevant recommendations to a user for items or different products. Recommendation systems usually produce a list of recommendations in one of two ways - through collaborative filtering or content-based filtering. In Collaborative filtering, it approaches building a model from a user's past activities (items that are previously purchased and/or numerical ratings given to those items) as well as similar decisions made by other users; then use that model to projection items (or ratings for items) that the user may have a concern in. The most popular recommendation applications in E- commerce are probably books, research articles, search queries, movies, music, news, social tags, and products in general. There are also recommendation systems for life insurance companies, jokes, experts, restaurants, financial services and Twitter followers. In this work, we are dealing of frequent item set based recommendation using Apriori Algorithm which works on concept of association rules. Example “If a customer purchases shirt then he also buys tie or pants in 70% of the cases”. The algorithm searches out frequently purchased items and those items are then suggested as a recommendation to the customer. 2. EXISTING SYSTEM Today, E-commerce sites use recommendation systems on a large scale to boost their business. The products can be recommended based on the extent of the overall sale with regards to a site, based on the suggestions to the customers, or based upon an analysis of the extra buying behavior of the customer, as a prediction for difficult buying behavior. This methodology is used by retailers all over the world to determine which items are purchased together. Also, they face cold start problem i.e. 1) How to recommend a new user in which case there is no browse history? 2) How to recommend new items which has no purchase history? It also gives recommendations based on the area of interests of the user, customer searches and also suggests products based on it. For e.g. Amazon or Flip cart uses user view data i.e. if any customer or user searches a product from a specific category the system suggests a product form the same category. Also based on the current search by the user, the site recommends products. Every user who visits the site may not buy a product. They can just go through it and based on those real-time search results the site recommend a product. 3. PROPOSED SYSTEM Apriori is designed to operate on databases containing transactions and generate association rules, while using a "bottom up" approach, which means that frequent subsets are extended one item at a time and groups of candidates (the candidate set contains all the frequent k-length item sets) which are tested against the data. The algorithm terminates when no further successful extensions are found. By generating sets of data, we calculate support and confidence of itemsets. We do not calculate support and confidence for itemsets which do not occur together to reduce redundancy of data. The process works in multiple iterations.