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Composing tree graphical models with persistent homology features for clustering mixed-type data

conference contribution
posted on 2023-06-09, 06:42 authored by Xiuyan Ni, Novi QuadriantoNovi Quadrianto, Yusu Wang, Chao Chen
Clustering data with both continuous and discrete attributes is a challenging task. Existing methods often lack a principled probabilistic formulation. In this paper, we propose a clustering method based on a tree-structured graphical model to describe the generation process of mixed-type data. Our tree-structured model factorizes into a product of pairwise interactions, and thus localizes the interaction between feature variables of different types. To provide a robust clustering method based on the tree-model, we adopt a topographical view and compute peaks of the density function and their attractive basins for clustering. Furthermore, we leverage the theory from topology data analysis to adaptively merge trivial peaks into large ones in order to achieve meaningful clusterings. Our method outperforms state-of-the-art methods on mixed-type data.

History

Publication status

  • Published

File Version

  • Published version

Journal

Proceedings of the 34th International Conference on Machine Learning

ISSN

1938-7228

Publisher

PMLR

Volume

70

Page range

2622-2631

Event name

International Conference on Machine Learning (ICML)

Event location

Sydney, Australia

Event type

conference

Event date

6 - 11 August 2017

Series

Proceedings on Machine Learning Research

Department affiliated with

  • Informatics Publications

Full text available

  • No

Peer reviewed?

  • Yes

Legacy Posted Date

2017-06-14

First Compliant Deposit (FCD) Date

2017-06-14

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