Multiple Trips Pattern Mining

Abstract

In recent years, photograph sharing is one of the most mainstream web service, for example, Flickr, trip advisor and numerous other web services. The photograph sharing web services give capacities to include Geo coordinates, tags, and user ID to photographs to make photograph organizing easily. This study focuses on Geotagged photographs and discusses an approach to recognize user multiple trips pattern, i.e., common arrangements of visits in towns and span of stay and also elucidating labels that describe the multiple trips pattern. First, we segment collection of photos into multiple trips and categorize the photos manually based on photo trips into multiple trips, themes such as Landmark, Nature, Business, Neutral and Event. Our method mines multiple trips pattern for multiple trips theme categories. The experimental result of our technique beats other methods and accurate segmentation of photo collections into numerous trips with the 85% of accuracy. The multiple trips categorize about 91% correctly using tags, photo id, titles of digital photos, user id and visited cities as features. In last, we demonstrate the motivating examples showing an application with which one can find multiple trips pattern from our datasets and other different queries visit duration, destination and multiple trips’ theme on trips.