Artificial neural networks for clustering and rule extraction
| Thesis title in Czech: | Umelé neuronové síte pro klastrování a extrakci pravidel |
|---|---|
| Thesis title in English: | Artificial neural networks for clustering and rule extraction |
| Academic year of topic announcement: | 2006/2007 |
| Thesis type: | diploma thesis |
| Thesis language: | angličtina |
| Department: | Department of Software Engineering (32-KSI) |
| Supervisor: | doc. RNDr. Iveta Mrázová, CSc. |
| Author: | hidden - assigned and confirmed by the Study Dept. |
| Date of registration: | 09.10.2006 |
| Date of assignment: | 09.10.2006 |
| Date and time of defence: | 21.05.2007 00:00 |
| Date of electronic submission: | 21.05.2007 |
| Date of proceeded defence: | 21.05.2007 |
| Opponents: | RNDr. Pavel Jiroutek |
| Guidelines |
| The student shall discuss the following areas in his diploma thesis:
- recapitulation and comparison of various different paradigms applicable to clustering and rule extraction - these will include especially models based on Kohonen self-organizing feature maps, RBF-networks, FCM-clustering and decision trees - pre-processing of the input data, visualization of the results, interpretation of extracted rules - adaptive and automatic detection of significant input parameters. Some of the above-stated areas should be discussed in more detail. Based on the chosen real-world data, the student shall propose a suitable strategy for rule extraction. Further, he shall implement the respective models and evaluate the obtained results. |
| References |
| 1. Some of the textbooks suitable for the chosen area, e.g.:
- M. Berry, G. Linoff: Data Mining Techniques For Marketing, Sales, and Customer Support, John Wiley & Sons, 1997 - R. Rojas: Neural Networks: A Systematic Introduction, Springer-Verlag, 1996 - S. Haykin: Neural Networks: A Comprehensive Foundation, Prentice Hall, Upper Saddle River, N. J., 1999 2. Relevant journal articles, e.g.: - M. Ishikawa: Rule Extraction by Successive Regularization, in: Neural Networks, Vol. 13, (2000), pp. 1171-1183. - T. Kohonen et al.: Self organization of a massive document collection, in: IEEE Transactions on Neural Networks, Vol. 11, No. 3, (May 2000), 574-586. - A. Weijters et al.: Behavioral Aspects of Combining Backpropagation Learning and Self-organizing Maps, in: Connection Science, Vol. 9, (1997), 235-252. 3. Current journals, e.g. Neurocomputing, Neural Networks, etc. |