Principal Coordinate Clustering

Date

2017-12-14

Author

SEKMEN, ali
ALDROUBİ, Akram
HAMM, Keaton
Koku, Ahmet Buğra

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This paper introduces a clustering algorithm, called principal coordinate clustering. It takes in a similarity matrix SW of a data matrix W and computes the singular value decomposition of SW to determine the principal coordinates to convert the clustering problem to a simpler domain. It is a relative of spectral clustering, however, principal coordinate clustering is easier to interpret, and gives a clear understanding of why it performs well. In a fashion, this gives intuition behind why spectral clustering works from a more simple, linear algebra perspective, beyond the typical explanations via graph cuts, or other techniques. Moreover, it was demonstrated through experimentation on real and synthetic data that the proposed method performs equally well on average as spectral clustering, and that the method has the ability to scale quite easily to truly large data.

Subject Keywords

Spectral clustering, Principal component analysis, Similarity matrix, Clustering

URI

https://hdl.handle.net/11511/55133

Collections

Department of Mechanical Engineering, Conference / Seminar

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Citation Formats

a. SEKMEN, A. ALDROUBİ, K. HAMM, and A. B. Koku, “Principal Coordinate Clustering,” 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55133.