• Aparna Annaso Patil
• Prathmesh S. Powar

Across online social networks, a sizable portion of society is now being brought onto the online platform. To build interconnection between people over great distances, this will be crucial. Such social media networks have proven to be among the most useful strategies for successfully fostering user interaction. Users can effortlessly exchange and communicate with a multitude of people. A great deal of information is produced by this relationship and the countless posts and other elements on social media websites, from which critical information may be extracted. This is problematic because to the huge volumes of data involved and the assumption that it would have been difficult to determine the connections and relationships between the data in order to retrieve it. For this, this paper explores relevant research and develops a successful method for converting data from social media into a graph database which use Neo4j, and also the implementation of term weight, Shannon information gain, and pattern protocol, including the ECLAT algorithm and fuzzy classification. This approach has been evaluated extensively through the use of experimentations which has achieved highly satisfactory results.

ECLAT, Shannon Information Gain, Fuzzy Classification, Neo4j.

"Frequent Pattern sub Graph generation on Online Social Network data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 9, page no.d13-d20, September-2022, Available :http://www.jetir.org/papers/JETIR2209304.pdf

"Frequent Pattern sub Graph generation on Online Social Network data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 9, page no. ppd13-d20, September-2022, Available at : http://www.jetir.org/papers/JETIR2209304.pdf