Implementation of clustering algorithm using graph embeddings and graph data science on Yelp restaurant dataset

dc.contributor.advisorHoare, Terrien
dc.contributor.authorKarangutkar, Sayali
dc.date.accessioned2021-04-28T19:05:54Z
dc.date.available2021-04-28T19:05:54Z
dc.date.issued2020
dc.description.abstractThis research uses the leading property graph DBMS, Neo4j to implement a Restaurant Knowledge Graph of the Yelp Dataset (Challenge 2020 – business; users; category; reviews). The application of CYPHER queries; graph algorithms for insight and graph embeddings for machine learning on the graph are presented. Recently released (April 2020) Version 1.3 of the Neo4j Graph Data Science library on Neo4j 4.1.0 is explored using the Python library Py2Neo. Use cases for the graph algorithms PageRank and Overlap Similarity are presented. It is shown that using Py2neo library, data can be prepared for the application of machine learning algorithms in Python. A graph embedding algorithm (Node2vec) is applied for clustering using a traditional k-Means clustering algorithm using Tableau. The results are visualized in Tableau.en
dc.identifier.citationKarangutkar, S. (2020). Implementation of clustering algorithm using graph embeddings and graph data science on Yelp restaurant dataset. Masters Thesis, Dublin Business School.en
dc.identifier.urihttps://esource.dbs.ie/handle/10788/4236
dc.language.isoenen
dc.publisherDublin Business Schoolen
dc.rightsItems in eSource are protected by copyright. Previously published items are made available in accordance with the copyright policy of the publisher/copyright holder.en
dc.rights.holderCopyright: The publisheren
dc.rights.urihttp://esource.dbs.ie/copyrighten
dc.titleImplementation of clustering algorithm using graph embeddings and graph data science on Yelp restaurant dataseten
dc.typeThesisen
dc.type.degreelevelMScen
dc.type.degreenameMSc in Data Analyticsen
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