AutoClass

Autoclass - A Bayesian Approach to Classification. We describe a Bayesian approach to the unsupervised discovery of classes in a set of cases, sometimes called finite mixture separation or clustering. The main difference between clustering and our approach is that we search for the “best” set of class descriptions rather than grouping the cases themselves. We describe our classes in terms of probability distribution or density functions, and the locally maximal posterior probability parameters. We rate our classifications with an approximate posterior probability of the distribution function w.r.t. the data, obtained by marginalizing over all the parameters. Approximation is necessitated by the computational complexity of the joint probability, and our marginalization is w.r.t. a local maxima in the parameter space. This posterior probability rating allows direct comparison of alternate density functions that differ in number of classes and/or individual class density functions. We discuss the rationale behind our approach to classification. We give the mathematical development for the basic mixture model, describe the approximations needed for computational tractability, give some specifics of models for several common attribute types, and describe some of the results achieved by the AutoClass program..


References in zbMATH (referenced in 70 articles )

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  1. Flores, M. Julia; Gámez, José A.; Martínez, Ana M.; Puerta, José M.: HODE: hidden one-dependence estimator (2009)
  2. Huang, Han-Shen; Yang, Bo-Hou; Chang, Yu-Ming; Hsu, Chun-Nan: Global and componentwise extrapolations for accelerating training of Bayesian networks and conditional random fields (2009) ioport
  3. Nielsen, Jens D.; Rumí, Rafael; Salmerón, Antonio: Supervised classification using probabilistic decision graphs (2009)
  4. Pfitzner, Darius; Leibbrandt, Richard; Powers, David: Characterization and evaluation of similarity measures for pairs of clusterings (2009) ioport
  5. Takahashi, Kazuko; Takamura, Hiroya; Okumura, Manabu: Direct estimation of class membership probabilities for multiclass classification using multiple scores (2009) ioport
  6. Zimmermann, Albrecht; De Raedt, Luc: Cluster-grouping: from subgroup discovery to clustering (2009)
  7. Glimcher, Leonid; Jin, Ruoming; Agrawal, Gagan: Middleware for data mining applications on clusters and grids (2008) ioport
  8. Kim, Hyoung-Rae; Chan, Philip K.: Learning implicit user interest hierarchy for context in personalization. (2008) ioport
  9. Sato, Taisuke; Kameya, Yoshitaka; Kurihara, Kenichi: Variational Bayes via propositionalized probability computation in PRISM (2008)
  10. Zhang, Nevin L.; Wang, Yi; Chen, Tao: Discovery of latent structures: experience with the CoIL challenge 2000 data set (2008)
  11. Domeniconi, Carlotta; Gunopulos, Dimitrios; Ma, Sheng; Yan, Bojun; Al-Razgan, Muna; Papadopoulos, Dimitris: Locally adaptive metrics for clustering high dimensional data (2007) ioport
  12. Jollois, F.-X.; Nadif, M.: Speed-up for the expectation-maximization algorithm for clustering categorical data (2007)
  13. Kim, Minkyong; Kotz, David: Periodic properties of user mobility and access-point popularity. (2007) ioport
  14. Ramamohanarao, Kotagiri; Fan, Hongjian: Patterns based classifiers (2007) ioport
  15. Vilalta, R.; Stepinski, T.; Achari, M.: An efficient approach to external cluster assessment with an application to Martian topography (2007) ioport
  16. Zhang, Yuping; Qian, Minping: The stochastic model and metastability of the gene network (2007)
  17. Allier, Bénédicte; Bali, Nadia; Emptoz, Hubert: Automatic accurate broken character restoration for patrimonial documents (2006) ioport
  18. Dong, Yihong; Zhuang, Yueting; Chen, Ken; Tai, Xiaoying: A hierarchical clustering algorithm based on fuzzy graph connectedness (2006)
  19. Kooptiwoot, S.; Salam, M. A.: IUI mining: human expert guidance of information theoretic network approach (2006) ioport
  20. Ling, Charles X.; Yang, Qiang: Discovering classification from data of multiple sources (2006) ioport