12
101
- 174 -
(ABS), Australian Bureau of Statistics. (2009). Labour Force, Australia. http://www.abs.gov.au/ausstats/[email protected]/mf/6202.0.
ABS, Australian Bureau of Statistics. (2009). Australian National Accounts: National
Income, Expenditure and Product.
http://www.abs.gov.au/ausstats/[email protected]/mf/5206.0/.
Adelson-Velskii, G. and E. Landis (1962). "An algorithm for the organization of information." Doklady Akademii Nauk SSSR(146): 263-266. (Russian). English translation by Myron J. Ricci in Soviet Math. Doklady, 3:1259–1263, 1962.
Amoroso, E. G. (1998). Intrusion detection : an introduction to Internet surveillance, correlation, traps, trace back, and response. Sparta, N.J., Intrusion.Net Books. AnderBerg, M. R. (1973). Cluster Analysis for Applications. New York, NY,
Academic Press, Inc.
Azé, J., N. Lucas and M. Sebag (2004). A Genetic ROC-based Classifier. Twenty- First International Conference on Machine Learning. Banff, Alberta, Canada. Bailey, M., E. Cooke, F. Jahanian, Y. Xu and M. Karir (2009). A Survey of Botnet
Technology and Defenses. Cybersecurity Applications & Technology Conference For Homeland Security(CATCH), Washington, DC, USA, IEEE Computer Society.
Bala, J., K. DeJong, J. Huang, H. Wechsler and H. Vafaie (1995). Hybrid learning using genetic algorithms and decision trees for pattern classification. . International Joint Conference on AI (IJCAI-95). Montreal, Quebec: 719- 724.
Ball, G. H. and D. J. Hall (1965). ISODATA: A Novel Method of Data Analysis and Pattern Classification. Technical Report. Menlo Park, CA, Stanford Research Institute.
BodyMedia (2004). Physiological Data Modeling Contest, ICML-2004 Workshop:
http://www.cs.utexas.edu/~sherstov/pdmc/.
Breiman, L. (1996). "Bagging Predictors." Machine Learning 24(2): 123-140.
Bruner, J. S., J. J. Goodnow and G. A. Austin (1956). A study of thinking. New York, John Wiley & Sons.
Corter, J. and M. Gluck (1992). "Explaining basic categories: Feature predictability and information." Psychological Bulletin 111(2): 291-303.
Dubes, R. C. (1987). "How many clusters are best?-an experiment." Pattern Recognition 20(6): 645-663.
Edmondson, R. (2009). Personal Communication, Head, Technology Services (Tas), Australian Bureau of Statistics, Hobart.
Feigenbaum, E. A. and H. A. Simon (1984). "EPAM-like models of recognition and learning." Cognitive Science 8(4): 305-336.
Fisher, D. and P. Langley (1985). Approaches to conceptual clustering. Ninth International Joint Conference on Artificial Intelligence, Los Angeles, CA, Morgan Kaufmann.
Fisher, D. and P. Langley (1990). "The structure and formation of natural categories." In G. H. Bower (Ed.), The psychology of learning and motivation: Advances in Research and Theory (Vol. 26).
Fisher, D., L. Xu, J. R. Carnes, Y. Reich, S. J. Fenves, J. Chen, R. Shiavi, G. Biswas and J. Weinberg (1993). "Applying AI Clustering to Engineering Tasks." IEEE Expert: Intelligent Systems and Their Applications 8(6): 51-60.
Fisher, D. H. (1987). "Knowledge Acquisition Via Incremental Conceptual Clustering " Machine Learning 2 (2 ): 139-172
Chapter 10 - References
Fisher, D. H. (1987). "Knowledge Acquisition Via Incremental Conceptual
Clustering." Machine Learning 2(2): 139-172.
Forgy, E. W. (1965). "Cluster Analysis of Multivariate Data: Efficiency versus
Interpretability of classification." Biometrics 21: 768-9.
Fuller, W. A. (1996). Introduction to statistical time series. New York, Wiley.
fyodor (2005). Nmap, http://nmap.org/.
Gama, J. and P. Rodrigues (2004). Physiological Data Modeling Contest. Twenty- First International Conference on Machine Learning. Banff, Alberta, Canada. Gamzu, E. and D. R. Williams (1971). "Classical conditioning of a complex skeletal
response." Science 171: 923-925.
Gennari, J. H., P. Langley and D. Fisher (1989). "Models of incremental concept
formation " Artif. Intell. 40 (1-3 ): 11-61
Gluck, M. and J. Corter (1985). Information, uncertainty and the utility of categories. Seventh Annual Conference of Cognitive Science Society, Irvine, CA.
Gluck, M. and J. Corter (1985). Information,uncertainty,and the utility of categories. Proceedings of the Seventh Annual Conference of the Cognitive Science
Society. Irvine,CA, Lawrence Erlbaum Associates: 283-287.
Hall, M., E. Frank, G. Holmes, B. Pfahringer, P. Reutemann and I. H. Witten (2009). "The WEKA Data Mining Software: An Update." SIGKDD Explorations
11(1).
Hansen, P. and M. Delattre (1978). "Complete-link cluster analysis by graph coloring
" Journal of the American Statistical Association 73: 397-403.
Hanson, S. J. and M. Bauer (1989). "Conceptual Clustering, Categorization, and
Polymorphy." Machine Learning 3(4): 343-372.
Hartigan, J. A. (1975). Clustering Algorithms, John Wiley & Sons, Inc.
Hunt, E. B., J. Martin and P. Stone (1966). Experiments in Induction. New York., Academic Press.
Iba, W. and P. Langley (2001). Unsupervised learning of probabilistic concept hierarchies. Machine learning and its applications. V. K. G. Paliouras, & C. D. Spyropoulos. Berlin, Springer.
Jain, A. K., M. N. Murty and P. J. Flynn (1999). "Data clustering: a review." ACM
Comput. Surv. 31(3): 264-323.
Jancey, R. C. (1966). "Multidimensional group analysis." Australian Journal of
Botany 14(1): 127-130.
Jung, J., V. Paxson, A. W. Berger and H. Balakrishnan (2004). Fast Portscan Detection Using Sequential Hypothesis Testing. IEEE Symposium on Security and Privacy.
Kilander, F. and C. G. Jansson (1993). COBBIT - A control procdure for COBWEB in the presence of concept drift. Proceedings of the European Conference on Machine Learning. P. B. Bradzil, Springer Verlag.
Klinkenberg, R. (2004). "Learning drifting concepts: example selection vs. example weighting." Intelligent Data Analysis, Special Issue on Incremental Learning
Systems Capable of Dealing with Concept Drift 8(3).
Kolodner, J. L. (1983). "Reconstructive memory: A computer model." Cognitive
Science 7(4): 281-328.
Kolter, J. Z. and M. A. Maloof (2003). Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift, IEEE Computer Society. Kubat, M. and J. Pavlickova (1991). The System FLORA: Learning from Type-
Varying Training Sets. Proceedings of the European Working Session on Machine Learning, Springer-Verlag.
- 176 -
Lebowitz, M. (1986). "Concept learning in a rich input domain:Generalization-based memory." Machine learning:An artificial intelligence approach 2.
Lebowitz, M. (1987). "Experiments with Incremental Concept Formation: UNIMEM." Machine Learning 2(2): 103-138.
Li, C., Q. Song and C. Zhang (2004). MA-IDS Architecture for Distributed Intrusion Detection using Mobile Agents. 2nd International Conference on Information Technology for Application, Harbin, China.
Li, M., G. Holmes and B. Pfahringer (2004). Clustering Large Datasets Using Cobweb and K-Means in Tandem 17th Australian joint conference on artificial intelligence (AI-04). T. D. Gedeon and L. C. C. Fung. Cairns, Australia, Springer-Verlag. 1: 368-379.
Liao, T. W. (2005). "Clustering of time series data--a survey." 38(11): 1857-1874. Lin, J., E. Keogh and W. Truppel (2003). Clustering of streaming time series is
meaningless. Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery. San Diego, California, ACM. Lin, W.-H. and A. Hauptmann (2004). Informedia at PDMC. Twenty-First
International Conference on Machine Learning. Banff, Alberta, Canada. MacQueen, J. B. (1967). Some methods for classification and analysis of
multivariate observations Proceedings of the 5th Berkeley Symposium Mathematical Statistical Probability. 1: 281-297.
McKusick, K. B. and P. K. Langley (1991). Constraints on tree structure in concept formation. Proceedings of the Twelfth International Joint Conference on Artificial Intelligence. Sydney, Australia, Morgan Kaufmann: 810-816. Medin, D. L., W. D. Wattenmaker and S. E. Hampson (1987). "Family resemblance,
conceptual cohesiveness, and category construction." Cognitive Psychology
19(2): 242-279.
Mervis, C. and E. Rosch (1981). "Categorization of natural objects." Annual Review of Psychology 32.
Michalski, R. S. (1974). Variable-valued logic: System VL1. Proceedings of the 1974 International Symposium on Multiple-Valued Logic. Morgantown, West Virginia. : 323-346.
Michalski, R. S. (1980). "Knowledge acquisition through conceptual clustering: A theoretical framework and an algorithm for partitioning data into conjunctive concepts." International Journal of Policy Analysis and Information Systems
4(3): 219-244.
Michalski, R. S. (1980). "Learning by being told and learning from examples: an experimental comparison of the two methodes of knowledge acquisition in the context of developing an expert system for soybean desease diagnoiss." International Journal of Policy Analysis and Information Systems 4(2): 25- 161.
Michalski, R. S. and R. E. Stepp (1981). An application of AI techniques to structuring objects into an optimal conceptual hierarchy. Vancouver, BC, Canada, Morgan Kaufmann Publishers Inc.
Michalski, R. S. and R. E. Stepp (1983). "Learning from observation: Conceptual clustering." Machine Learning: an artificial intelligence approach 1: 331–363. Michalski, R. S., R. E. Stepp and E. Diday (1981). "A recent advance in data analysis: Clustering objects into classes characterized by conjunctive concepts." Invited chapter in the book Progress in Pattern Recognition, L. Kanal and A. Rosenfeld (Eds.) 1: 33-55.