Abstract
Association rules that represent isomorphisms among data have gained importance in exploratory data analysis because they can find inherent, implicit, and interesting relationships among data. They are also commonly used in data mining to extract the conditions among attribute values that occur together frequently in a dataset [1]. These rules have wide range of applications, namely in the financial and retail sectors of marketing, sales, and medicine.
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References
Dua, S., Singh, H., Thompson, H.W.: Associative Classification of Mammograms using Weighted Rules based Classification, Expert Systems With Applications, doi:doi:10.1016/j.eswa.2008.12.050
http://archive.ics.uci.edu/ml/datasets/Artificial+Characters
Witten, I., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
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Dua, S., Singh, H. (2009). Association Rule Based Feature Extraction for Character Recognition. In: Prasad, S.K., Routray, S., Khurana, R., Sahni, S. (eds) Information Systems, Technology and Management. ICISTM 2009. Communications in Computer and Information Science, vol 31. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00405-6_48
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DOI: https://doi.org/10.1007/978-3-642-00405-6_48
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