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Abstract: . A large volume of concrete data may change over time in a database. It is important to catch the general trend of such changes and find data evolution (changing) regularities in databases in many applications. Because of the large volume of data, data evolution regularity cannot be simply expressed by enumeration of actual data. Machine learning technology should be adopted to extract such regularities in databases. This paper describes an attribute-oriented induction technique for discovery... (Update)
Context of citations to this paper: More
...interest to the user. There are many other approaches to data mining or knowledge discovery. All these approaches and systems [PSF91, MCPS93, HCCH94] share the premise that there is knowledge hidden in vast amounts of information and that this knowledge can be discovered and...
...with numbers, not with symbolic data. CHAPTER 9. LEARNING PROBABILISTIC FQL 128 7. In contrast to other mining techniques such as [43, 44, 65], DISCOVER can also deal with weighted information. From above list, the experimental results and the sophisticated functionality...
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BibTeX entry: (Update)
J. Han, Y. Cai, N. Cercone, and Y. Huang. Discovery of Data Evolution Regularities in Large Databases. Journal of Computer and Software Engineering, 1994. http://citeseer.ist.psu.edu/han94discovery.html More
@misc{ han94discovery,
author = "J. Han and Y. Cai and N. Cercone and Y. Huang",
title = "Discovery of Data Evolution Regularities in Large Databases",
text = "J. Han, Y. Cai, N. Cercone, and Y. Huang. Discovery of Data Evolution Regularities
in Large Databases. Journal of Computer and Software Engineering, 1994.",
year = "1994",
url = "citeseer.ist.psu.edu/han94discovery.html" }
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