Data Clustering via Principal Direction Gap Partitioning
Machine Learning
2012-11-20 v1 Machine Learning
Abstract
We explore the geometrical interpretation of the PCA based clustering algorithm Principal Direction Divisive Partitioning (PDDP). We give several examples where this algorithm breaks down, and suggest a new method, gap partitioning, which takes into account natural gaps in the data between clusters. Geometric features of the PCA space are derived and illustrated and experimental results are given which show our method is comparable on the datasets used in the original paper on PDDP.
Keywords
Cite
@article{arxiv.1211.4142,
title = {Data Clustering via Principal Direction Gap Partitioning},
author = {Ralph Abbey and Jeremy Diepenbrock and Amy Langville and Carl Meyer and Shaina Race and Dexin Zhou},
journal= {arXiv preprint arXiv:1211.4142},
year = {2012}
}