Prediction on Clusters by using Information Criterion and Multiple Seeds
Young-Hee Cho, Gye-Sung Lee · The Journal of the Institute of Webcasting, Internet and Telecommunication · 2010
Bayesian information criterion is used to do clustering for time series data. To acquire more stable clusters, multiple seeds are chosen first for the algorithm. Once clusters being set up, most similar time series data in the cluster to the one under consideration are to be chosen for prediction test. These chosen time series data are used to extract valid Markov rules by which we test the prediction accuracy. We confirmed that clustering with multiple seeds led to better prediction performance.