ICML 2016poster16 citations
Clustering High Dimensional Categorical Data via Topographical Features
Abstract
Analysis of categorical data is a challenging task. In this paper, we propose to compute topographical features of high-dimensional categorical data. We propose an efficient algorithm to extract modes of the underlying distribution and their attractive basins. These topographical features provide a geometric view of the data and can be applied to visualization and clustering of real world challenging datasets. Experiments show that our principled method outperforms state-of-the-art clustering methods while also admits an embarrassingly parallel property.
BibTeX
@InProceedings{pmlr-v48-chenc16,
title = {Clustering High Dimensional Categorical Data via Topographical Features},
author = {Chen, Chao and Quadrianto, Novi},
booktitle = {Proceedings of The 33rd International Conference on Machine Learning},
pages = {2732--2740},
year = {2016},
editor = {Balcan, Maria Florina and Weinberger, Kilian Q.},
volume = {48},
series = {Proceedings of Machine Learning Research},
address = {New York, New York, USA},
month = {20--22 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v48/chenc16.pdf},
url = {https://proceedings.mlr.press/v48/chenc16.html},
abstract = {Analysis of categorical data is a challenging task. In this paper, we propose to compute topographical features of high-dimensional categorical data. We propose an efficient algorithm to extract modes of the underlying distribution and their attractive basins. These topographical features provide a geometric view of the data and can be applied to visualization and clustering of real world challenging datasets. Experiments show that our principled method outperforms state-of-the-art clustering methods while also admits an embarrassingly parallel property.}
}