MPhil Thesis Defence


"Time Series Clustering Using ARMA Models"

By

Mr. Yimin Xiong


Abstract

Clustering problems are central to many knowledge discovery and data
mining tasks. However, most existing clustering methods can only work with
fixed-dimensional representations of data patterns. In this paper, we
study the clustering of data patterns that are represented as sequences or
time series possibly of different lengths.

We first propose a model-based method to this problem using mixtures of
autoregressive moving average (ARMA) models. We derive an
expectation-maximization (EM) algorithm for learning the mixing
coefficients as well as the parameters of the component models. To address
the model selection problem, we use the Bayesian information criterion
(BIC) to determine the number of clusters in the data. Experiments were
conducted on a number of simulated and real datasets. Results from the
experiments show that our method compares favorably with other methods
proposed previously by others for similar time series clustering tasks.

In the second part of the thesis, we present an experimental comparison of
several distance measures for ARMA models in the context of time series
clustering.  Four types of ARMA distance measures are considered,
including measures defined based on linear predictive coding (LPC)
cepstral coefficients, subspace angles, Fisher scores, and information
theoretic distance measures. These distance measures are used with both
partitional and hierarchical clustering algorithms in our experiments
involving both simulated and real electroencephalogram (EEG) data.
Experimental results show that information theoretic measures,
particularly the Bhattacharya distance measure and the Kullback-Leibler
divergence, generally outperform other distance measures in clustering
quality as measured by the Rand index.


Date:				Wednesday, 18 August 2004

Time:				1:30p.m.-3:30p.m.

Venue:				Room 2406
				Lifts 17-18

Committee Members:		Prof. Dit-Yan Yeung (Supervisor)
				Prof. Nevin Zhang (Chairperson)
				Prof. James Kwok



**** ALL are Welcome ****