CheQua16.pdf (900.16 kB)
Clustering high dimensional categorical data via topographical features
conference contribution
posted on 2023-06-09, 01:28 authored by Chao Chen, Novi QuadriantoNovi QuadriantoAnalysis 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.
History
Publication status
- Published
File Version
- Accepted version
Journal
Proceedings of the 33rd International Conference on Machine Learning; New York; 19 - 24 June 2016ISSN
1938-7288Publisher
JMLRPublisher URL
Volume
48Page range
2732-2740Event name
International Conference on Machine LearningEvent location
New York, New York, USAEvent type
conferenceEvent date
20-22 June 2016Department affiliated with
- Informatics Publications
Full text available
- Yes
Peer reviewed?
- Yes
Editors
Maria Florina Balcan, Kilian Q WeinbergerLegacy Posted Date
2016-06-03First Open Access (FOA) Date
2016-09-23First Compliant Deposit (FCD) Date
2016-06-03Usage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC