LEED Certification Prediction with K-Means Clustering Algorithm

Authors

Jack Chase, Kenyon CollegeFollow

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

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.