Customized Trip Planning Using Modified Clustering Algorithm with Personalized Points of Interests and Association Rule Generation

Abstract

Data mining technologies are widely used to mine the interesting patterns and data from the huge collections of data. This work is intended to develop and implement an efficient, economic and customized software for trip plan for final customers. The main functions of this software are listed in the following. In this itinerary planning system, the users specify source, destination, days of travel and budget, then the system will generate a set of Points Of Interests (POIs) for a given destination, so the users can select their customized points of interests and can plan travels with k-days itinerary. All POIs are considered and ranked based on the users preference. Nearby POIs are put in the same day itinerary for the effective utilization of the plan. Inconsistent and incomplete data are removed and the list of POIs will be generated using the Clustering technique so that users can select their interested POIs. Shortest path will be displayed once they select their customized POIs. Frequent visits and suggestions for a particular destination are listed to the customers using Association rule mining technique. Clustering takes major advantage to compute the data and it has fast processing time. Each cluster holds the number of countries with the relevant information and the list of points of interests for a specified cost. With the help of shortest path, users can understand and view what is the travel distance between the POIs. Users can delete the POIs if they have relatively long distance to visit and travel. Using the association rule mining technique users can know the frequent visits of a particular destination, and it helps the system to predict the travelling patterns of the people. In this work, a customized itinerary planning service is provided, and the users can plan for multiday itineraries, designed effectively by managing the cost and time to the users.
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