| Even with the current state of technology, data growth is increasing so fast that without proper storage and analytical techniques, it is increasingly challenging to process and analyze large datasets. This applies to knowledge bases from all fields, but for the purpose of this paper, we will be discussing specifically the area of professional wine reviews in a new area we call Wine Informatics. In this area, we gathered over one thousand professional wine tasting reviews and manually extracted key attributes that we felt defined a wine. These attributes were categorized in three major ways: savory flavor attributes, physical characteristics, and overall subjective descriptors. The extraction process led to the creation of what we call a computational wine wheel, which is a wine attribute dictionary consisting of 899 categorized and normalized wine attributes, as well as a weight system to define a level of importance. We applied Hierarchical Clustering, BiMax Biclustering, and a proposed TriMax Triclustering algorithm onto various wine review datasets formed around the computational wine wheel. We found that all three clustering methods produced promising and cohesive results that can be used in various aspects of the wine industry, such as defined palate grouping and wine searching. |
