From Sparse Data-sets to Graphs: When Explicit Relationships Bridge the Gaps
The various ways we frame a problem have significant impact on how we approach it. From the questions we ask to the tools we use. A simple change can have great repercussions and be of significant benefit to how you tackle the issue at hand.
At EB Games, in trying to develop a customer segmentation/targeting model, we struggled with traditional clustering algorithms on tabular data. All the necessary information was there, but the resulting datasets were so sparse that results were difficult to come by. When we changed to a graph based model, it greatly increased the ease with which we could ask questions and add further detail. The power afforded to us by having explicit relationships meant that suggestions and ideas from subject matter experts were more easily translated into something that could be quantified and/or qualified.
In this presentation I will share the process and journey of this project and provide insights on the benefits gained from using a different structure to store and analyse your data.
Data Analysts/Scientists working with data where relationships between entities is relevant
Prerequisites for Attendees
Basic understanding of clustering algorithms and/or graph theory and graph storage/analysis is helpful but not necessary.