Adaptive knowledge discovery techniques for data mining
dc.contributor.advisor  Purvis, Martin  
dc.contributor.advisor  Cranefield, Stephen  
dc.contributor.advisor  Kasabov, Nikola  
dc.contributor.author  Zhou, Qing Qing  en_NZ 
dc.date.available  20110407T03:17:04Z  
dc.date.copyright  200307  en_NZ 
dc.identifier.citation  Zhou, Q. Q. (2003, July). Adaptive knowledge discovery techniques for data mining (Thesis, Doctor of Philosophy). Retrieved from http://hdl.handle.net/10523/1492  en 
dc.identifier.uri  http://hdl.handle.net/10523/1492  
dc.description.abstract  The ease of collection and the increasing availability of large data stores has led to demands for improved methods for analyzing these data and deriving significant knowledge that maybe latent in these data stores. In particular there is hope that the use of new analytical techniques in connection with "data trawling", or data mining operations may reveal hidden relationships that lie buried within these data sets. This research investigates various techniques for the task of discovering relevant features and inference rules from data sets. Following the three steps of a knowledge extraction process, namely preprocessing (feature selection), rule discovery process, and postprocessing (rule refinement), the research attempts to address some current difficulties in these three steps and introduces and integrates a °market trading' technique with existing techniques from the field of knowledge discovery and refinement with respect to data mining. In connection with the preprocessing, a feature selection approach that employs neural networks is presented, and three associated pruning schemes that make automatic selection of the pruning threshold are proposed. The proposed neural network techniques are evaluated and compared with the x2statisticbased discretization algorithm, called Chi2, by experimenting with six practical applications. The Chi2 algorithm is investigated as a technique for solving problems in intelligent spatial information systems and fuzzy systems. The case studies show that the Chi2based spatial data filtering can successfully reduce the number of spatial data items and the number of features, and therefore neural network computation can be efficiently performed. A novel approach of employing the Chi2 algorithm to select membership functions for fuzzy systems is proposed. In connection with the applications of fuzzy neural networks (FuNN models), three experimental examinations are demonstrated that an automatic selection of the number and widths of the membership functions by the Chi2based membership function selection method can lead to the improvement of the generalization ability of FuNN fuzzy neural networks. In connection with the rule discovery and refinement process, a novel marketbased rule learning (MBRL) system is developed and its capability of evolving and refining rules is investigated. a classifier systeminspired model, it introduces a novel element by importing existing rule sets generated by other rule extraction techniques into the system. This basic change not only makes the RL system begin with preestablished rule sets with a relatively limited complexity, rather than a random set, but also enhances the likelihood of being able to interpret the evolved rules. Moreover, the MBRL system produces various modifications in each of the layers of the structure. With the modifications introduced by the MBRL system, the problems existing in current classifier systems can be solved or lessened. In this research, the MBRL system is proposed as a postprocessing tool to be used with fuzzy neural networks (FuNN models) and the fuzzy neural network rule extraction technique, ReFuNN, in order to provide a general framework for fuzzy inferencebased rule discovery. Similarly, as a postprocessing tool, the MBRL system is also proposed to be used with feedforward neural networks, and the feedforward neural network rule extraction technique, NeuroLinear, in order to improve the quality of extracted rules from feedforward neural networks. The experimental results show that the MBRL system is a potentially useful additional tool that can be used to refine (fuzzy) neural network extracted rules and possibly discover and add some new, better performance rules. As a result, it can lead to improved performance by increasing the accuracy of the rule inference performance and/or improving the comprehensibility of the rules. By illustrating how the MBRL system succeeded in finding solutions for six learning examples from scratch, the MBRL system is shown to have potential as an alternative generic learning technique that can be used to complement, or be used as an alternative to, conventional connectionist models to accomplish complex computational tasks.  en_NZ 
dc.subject  data stores  en_NZ 
dc.subject  data trawling  en_NZ 
dc.subject  neural network techniques  en_NZ 
dc.subject  fuzzy neural networks  en_NZ 
dc.subject  marketbased rule learning  en_NZ 
dc.subject  complex computational tasks  en_NZ 
dc.subject  Data mining  en_NZ 
dc.subject  feedforward neural networks  en_NZ 
dc.subject.lcsh  T Technology (General)  en_NZ 
dc.subject.lcsh  Q Science (General)  en_NZ 
dc.title  Adaptive knowledge discovery techniques for data mining  en_NZ 
dc.type  Thesis  en_NZ 
dc.description.version  Unpublished  en_NZ 
otago.date.accession  20070416  en_NZ 
otago.school  Information Science  en_NZ 
thesis.degree.discipline  Information Science  en_NZ 
thesis.degree.name  Doctor of Philosophy  
thesis.degree.grantor  University of Otago  en_NZ 
thesis.degree.level  Doctoral Theses  en_NZ 
otago.interloan  yes  en_NZ 
otago.openaccess  Abstract Only  
dc.identifier.eprints  551  en_NZ 
otago.school.eprints  Information Science  en_NZ 
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