Global likelihood optimization via the cross-entropy method with an application to mixture models
WSC '04 Proceedings of the 36th conference on Winter simulation
Predictive Modeling of Large-Scale Sequential Curves Based on Clustering
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part II
New global optimization algorithms for model-based clustering
Computational Statistics & Data Analysis
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In this paper we propose two new EM-type algorithms for model-based clustering. The first algorithm, Ascent EM, draws its ideas from the Monte Carlo EM algorithm and uses only random subsets from the entire database. Using only a subset rather than the entire database allows for significant computational improvements since many fewer data points need to be evaluated in every iteration. We also argue that one can choose the subsets intelligently by appealing to EMs highly-appreciated likelihood-ascent property. The second algorithm that we propose builds upon Ascent EM and incorporates ideas from evolutionary computation to find the global optimum. Model-based clustering can feature local, sub-optimal solutions which can make it hard to find the global optimum. Our algorithm borrows ideas from the Genetic Algorithm (GA) by incorporating the concepts of crossover, mutation and selection into EMs updating scheme. We call this new algorithm the GA Ascent EM algorithm. We investigate the performance of these two algorithms in a functional database of online auction price-curves gathered from eBay.com.