Learning Bayesian networks for clustering by means of constructive induction |
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| Affiliation: | 1. Gregorio Marañón General University Hospital, Department of Otorhinolaryngology, Madrid, Spain;2. Gregorio Marañón General University Hospital, Department of Microbiology and Infectious Disease, Madrid, Spain;1. Paris Diderot University, Hôpital Lariboisière, Department of Otorhinolaryngology, AP-HP, Paris, France;2. Paris Diderot University, AP-HP, Hôpital Lariboisière, Department of Pathology, Paris, France;1. Department of Statistics and Finance, University of Science and Technology of China, Hefei, Anhui, 230026, China;2. College of Finance and Statistics, Hunan University, Changsha, Hunan, 410082, China;3. School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, 200433, China;4. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China;1. Servicio de Nefrología, Hospital Universitario Valdecilla, Universidad de Cantabria, Santander, Cantabria, Spain;2. Servicio de Nefrología, Hospital Clinic, Barcelona, Spain;3. Servicio de Oncología Médica, Hospital Virgen del Rocío, Sevilla, Spain;4. Servicio de Radiodiagnostico, Hospital Clinic, Barcelona, Spain |
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| Abstract: | The purpose of this paper is to present and evaluate a heuristic algorithm for learning Bayesian networks for clustering. Our approach is based upon improving the Naive-Bayes model by means of constructive induction. A key idea in this approach is to treat expected data as real data. This allows us to complete the database and to take advantage of factorable closed forms for the marginal likelihood. In order to get such an advantage, we search for parameter values using the EM algorithm or another alternative approach that we have developed: a hybridization of the Bound and Collapse method and the EM algorithm, which results in a method that exhibits a faster convergence rate and a more effective behaviour than the EM algorithm. Also, we consider the possibility of interleaving runnings of these two methods after each structural change. We evaluate our approach on synthetic and real-world databases. |
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