Discovery of Quantified Hierarchical Production Rules from Large Set of Discovered Rules
Automated discovery of Rule is, due to its applicability, one of the most fundamental and important method in KDD. It has been an active research area in the recent past. Hierarchical representation allows us to easily manage the complexity of knowledge, to view the knowledge at different levels of details, and to focus our attention on the interesting aspects only. One of such efficient and easy to understand systems is Hierarchical Production rule (HPRs) system. A HPR, a standard production rule augmented with generality and specificity information, is of the following form: Decision If < condition> Generality
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1076668Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1229
 Bharadwaj, K.K., Neerja and Goel, G.C. 1994, ÔÇÿHierarchical Censored Production Rules system employing Dampster-Shafer Uncertainty Calculus-, Information and Software Technology, Vol 36, pp 155-174.
 Tamanna Siddiqui, K. K. Bharadwaj, "Discovery of Quantified Censored Production Rules from the Large set of Discovered rules", Proceedings of International Conference: Conference on Information Science, Technology and Management (CISTM 2006), Chandigarh, India. July 16-18, 2006.
 S. Levachkine and A. Guzman-Arenas, "Hierarchies measuring qualitative variables," Springer-Verlag Berlin Heidelberg 2004, A. Gelbukh (Ed.):CICLing 2004,2004,pp.262-274.
 B. Liu., M. Hu. And W. Hsu., " Inductive Representation of decision Trees using general rules and exceptions", AAAI-2000.
 Cios, K. J. Sztandera, L. M., "Continuous ID3 Algorithm with Fuzzy entropy measures. Proc. IEEE Int. Conf. Fuzzy Systems, San Diego, 469-476, 1996.
 U. Fayyad, G. P. Shapiro and P Smyth, "The KDD process for extracting useful knowledge from volumes of data", Communications of the ACM, vol.39, pp. 27-34, 1996.
 K. Sentz and S. Ferson (2002), ÔÇÿCombination of Evidence in Dempster- Shafer Theory-, Sandia National Laboratories report SAND2002-0835.
 Farhad Hussain, Huan Liu, Einoshin Suzuki, Hongjun Lu: Exception Rule Mining with a Relative Interestingness Measure. PAKDD 2000: 86-97.
 M. Suan, "Semi-Automatic taxonomy for efficient information searching," Proceeding of the 2nd International Conference on Information Technology for Application (ICITA-2004), 2004.
 J. R. Koza, "Genetic programming: on the programming of computers by means of natural selection," MIT Press, 1994.
 C. C. Bojarczuk, H. S. Lopes, and A. A. Freitas, " Genetic programming for knowledge discovery in chest pain diagnosis," IEEE Engineering in Medical and Biology magazine-special issue on data mining and knowledge discovery, 19(4), July/Aug 2000,pp.38-44.
 Tamanna Siddiqui, "A KDD Tool for automated Discovery of knowledge", Proceedings of the 2nd national Conference INDIA Com - 2008, Computing for nation development, February 08 - 09, 2008.
 223012 (M01) Statistical quantification of the sources of variance in uncertainty analysis: Robinson R.B., Hurst B.T., Risk Analysis, Volume 17, Nr. 4, pp 447-454
 K. K. Bharadwaj and R. Varshneya, "Parallelization of hierarchical censored production rules," Information and Software Technology, 37, 1995, pp.453-460.
 K. K. Bharadwaj and N. K. Jain, "Hierarchical censored production rules (HCPRs) systems," Data and Knowledge Engineering, North Holland, vol. 8, 1992, pp.19-34.
 J. Han, and Y. FU, "Dynamic generation and refinement of concept hierarchies for knowledge discovery in databases," AAAI-94 Workshop Knowledge in Databases (KDD-94), Seattle, WA, July 1994, pp. 157- 168.
 H. Surynato and P. Compton, "Learning classification taxonomies from a classification knowledge based system," Proceedings the First Workshop on Ontology Learning in Conjunction with ECAI-2000, Berlin, pp.1-6.
 B. Liu, M. Hu, and W. Hsu, "Multi-level organization and summarization of the discovered rules," Boston, USA, SIGKDD-2000, Aug 20-23, 2000.
 D. Richards and U. Malik, "Multi-level rule discovery from propositional knowledge bases," International Workshop on Knowledge Discovery in Multimedia and Complex Data (KDMCD-02), Taipei, Taiwan, May 2002, pp.11-19.
 A. A. Freitas, "A survey of evolutionary algorithms for data mining and knowledge discovery," In: A. Ghosh, and S. Tsutsui (Eds.) Advances in Evolutionary Computation, Springer-Verlag, 2002.
 I. De Falco, A. Della Cioppa, and E. Tarantiono, "Discovering interesting classification rules with genetic programming," Applied Soft Computing, 1, 2002, pp.257-269.
 M. V. Fidelis, H. S. Lopes, and A. A. Freitas, "Discovering comprehensible classification rules with a genetic algorithm," Proc. Congress on Evolutionary Computation-2000 (CEC-2000), La Jolla, CA, USA,IEEE, July 2000, pp.805-810.