IPHS 300: Artificial Intelligence for the Humanities: Text, Image, and Sound
Document Type
Poster
Publication Date
Fall 2018
Abstract
This project uses a K-Means Clustering algorithm. K-Means Clustering is a method of vector quantization, originally from signal processing that is popular for cluster analysis in data mining. K-Means Clustering aims to partition n observations into kclusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. The example above has three clusters, and my project used four clusters, one for each LEED Certification. I
Recommended Citation
Chase, Jack, "LEED Certification Prediction with K-Means Clustering Algorithm" (2018). IPHS 300: Artificial Intelligence for the Humanities: Text, Image, and Sound. Paper 3.
https://digital.kenyon.edu/dh_iphs_ai/3
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