Parallel Semi‐supervised enhanced fuzzy Co‐Clustering (PSEFC) and Rapid Association Rule Mining (RARM) based frequent route mining algorithm for travel sequence recommendation on big social media

Summary In this proposed method, with the aim of resolving the frequent route mining issue, by means of utilizing the Rapid Association Rule Mining (RARM) for frequent route mining, recurrently utilized routes as well as smaller distance routes are mined. As a result, support speedy decision of the route identify more willingly than the standard route optimization. Primarily, Multi‐Ontology based Points of Interest (MO‐POIs) model is presented that takes the POIs of user design in combination with the semantic info of the individual users. Furthermore, it as well takes another two steps that are along these lines: (1) routes ranking as per the similarity amid user package as well as routes packages are carried out by means of utilizing Parallel Semi‐supervised enhanced fuzzy Co‐Clustering (PSEFC) as well as (2) route optimizing by Parallel Ant Colony Optimization (PACO) technique in keeping with identical social users' records. The graph model is denoted as the amount of ants in the population of the identical user records. Assess the RARM‐PSEFC recommendation system on a set of Flickr images uploaded by users as well as travel POIs in numerous cities and show its efficiency.