Abstract
Modern data bases have vast information and their manual analysis for the purpose of knowledge discovery is almost impossible. Today the requirement of automatic extraction of useful knowledge among large-capacity data is completely realized. Consequently, the automatic analysis and data discovery tools are in progress rapidly. Data mining is a knowledge that analyzes extensive level of unstructured data and helps discovering the required connections for better understanding of fundamental concepts. On the other sides, temporal data mining is related to the analysis of sequential data streams with temporal dependence. The purpose of temporal data mining is detection of hidden patterns in either unexpected behaviours or other exact connections of data. Hitherto various algorithms have been presented for temporal data mining. The aim of present study is to introduce, collect and evaluate these algorithms to create a global view over temporal data mining analyses. According to significant importance of temporal data mining in diverse practical applications, our suggestive collection can be considerably beneficial in selecting the appropriate algorithm.
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References
Goebel, M., Gruenwald, L.: A Survey of Data Mining and Knowledge Discovery Software Tools (1999)
Shapiro, G.P., Frawley, W.J.: Knowledge Discovery in Databases. AAAI/MIT Press (1991)
Feelders, A., Daniels, H., Holsheimer, M.: Methodological and Practical Aspects of Data Mining (2000)
Bellazzi, R., Larizza, C., Magni, P., Bellazzi, R.: Temporal Data Mining for The Quality Assessment of Hemodialysis Services. Artificial Intelligence in Medicine 34, 25–39 (2004)
Laxman, S., Sastry, S.: A Survey of Temporal Data Mining. Sadhana 31(2), 173–198 (2006)
Chen, X., Petrounias, I.: An Architecture for Temporal Data Mining. In: IEE Colloquium on Knowledge Discovery and Data Mining, vol. 310, pp. 8/1–8/4. IEEE (1998)
Hand, D., Mannila, H., Smyth, P.: Principles of Data Mining. MIT Press, Cambridge (2001); Published by Asoke K
Gopalan, N.P., Sivaselvan, B.: Data Mining: Techniques and Trends. A.K. Ghosh, New Delhi (2009); Published by A.K. Ghosh
Gharib, T.F., Nassar, H., Taha, M., Abraham, A.: An Efficient Algorithm for Incremental Mining of Temporal Association Rules. Journal of Data & Knowledge Engineering 69, 800–815 (2010)
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: 20th International Conference on Very Large Data Bases (VLDB 1994), pp. 487–499 (1994)
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© 2012 Springer-Verlag GmbH Berlin Heidelberg
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Keyvanpour, M.R., Etaati, A. (2012). Analytical Classification and Evaluation of Various Approaches in Temporal Data Mining. In: Thaung, K. (eds) Advanced Information Technology in Education. Advances in Intelligent and Soft Computing, vol 126. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25908-1_39
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DOI: https://doi.org/10.1007/978-3-642-25908-1_39
Publisher Name: Springer, Berlin, Heidelberg
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