Comparative Analysis of Optimized Algorithms for Ontology Clustering
Avantika Tiwari, Ajay Kumar · 2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) · 2018
Every organization has to deal with abundant of data in its day-to-day proceedings. With time and work this collection of raw data and information also keeps growing. For reasons like decision making, finding co-relations or figuring out differences among the data it is mandatory to traverse through the whole huge collection of data. Examining such huge data collections or complex ontologies in context of semantic web may take too long and will be additional overhead. Along with extra time period it may occupy larger space as well. To replace the already existing data mining techniques another simpler approach has been introduced known as Clustering. Clustering is the scheme of placing together similar items in one cluster and separating the dis-similar ones. Clustering technique decomposes complex ontologies into simpler small data sets. These decomposed data sets save time and memory when it comes to information retrieval or query processing in semantic web. This paper proposed two optimized algorithms K-ENRICH and FC-ENRICH for ontology clustering and comparative analysis of these algorithms with some existing algorithms using various attributes that are F-measure, similarity and entropy.