Sequential Data Clustering

Author

Wu, Jianfei ; Nimer, L.A. ; Azzam, O.A. ; Chitraranjan, Charith ; Salem, Saeed ; Denton, Anne M.

Author_Institution

Dept. of Comput. Sci. & Oper. Res., North Dakota State Univ., Fargo, ND, USA

fYear

2010

fDate

12-14 Dec. 2010

Firstpage

1015

Lastpage

1020

Abstract

An algorithm is presented for clustering sequential data in which each unit is a collection of vectors. An example of such a type of data is speaker data in a speaker clustering problem. The algorithm first constructs affinity matrices between each pair of units, using a modified version of the Point Distribution algorithm which is initially developed for mining patterns between vector and item data. The subsequent clustering procedure is based on fitting a Gaussian mixture model on multiple random projection matrices. The final class label of each unit is determined by voting from the results of the random projection matrices.

Keywords

Gaussian processes; data mining; matrix algebra; pattern clustering; random processes; speech processing; vectors; Gaussian mixture model; affinity matrices; item data; mining patterns; multiple random projection matrices; point distribution algorithm; sequential data clustering; speaker clustering problem; speaker data; subsequent clustering procedure; vector data; Clustering algorithms; Equations; Kernel; Mathematical model; Speech; Symmetric matrices; Time series analysis; Gaussian Mixture model; KL-divergence; Point Distribution algorithm; Random projection;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Applications (ICMLA), 2010 Ninth International Conference on

Conference_Location

Washington, DC

Print_ISBN

978-1-4244-9211-4

Type

conf

DOI

10.1109/ICMLA.2010.161

Filename

5708987