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 socialmedia

摘要

In this proposed method, with the aim of resolving the frequent route mining issue, by means ofutilizing the Rapid Association Rule Mining (RARM) for frequent route mining, recurrently utilizedroutes as well as smaller distance routes are mined. As a result, support speedy decision ofthe route identify more willingly than the standard route optimization. Primarily,Multi-Ontologybased Points of Interest (MO-POIs)model is presented that takes the POIs of user design in combinationwith the semantic info of the individual users. Furthermore, it as well takes another twosteps that are along these lines: (1) routes ranking as per the similarity amid user package aswell as routes packages are carried out by means of utilizing Parallel Semi-supervised enhancedfuzzy Co-Clustering (PSEFC) as well as (2) route optimizing by Parallel Ant Colony Optimization(PACO) technique inkeepingwith identical social users' records.Thegraphmodelisdenoted as theamount of ants in the population of the identical user records. Assess the RARM-PSEFC recommendationsystem on a set of Flickr images uploaded by users as well as travel POIs in numerouscities and show its efficiency.