Abstract
We propose the use of maximal frequent itemsets (MFIs) to derive association rules from tabular datasets. We first present an efficient method to derive MFIs directly from tabular data using the information from previous search, known as tail information. Then we utilize tabular format to derive MFI, which can reduce the search space and the time needed for support-counting. Tabular data allows us to use spreadsheet as a user interface. The spreadsheet functions enable users to conveniently search and sort rules. To effectively present large numbers of rules, we organize rules into hierarchical trees from general to specific on the spreadsheet Experimental results reveal that our proposed method of using tail information to generate MFI yields significant improvements over conventional methods. Using inverted indices to compute supports for itemsets is faster than the hash tree counting method. We have applied the proposed technique to a set of tabular data that was collected from surgery outcomes and that contains a large number of dependent attributes. The application of our technique was able to derive rules for physicians in assisting their clinical decisions.
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Zou, Q., Chen, Y., Chu, W., Lu, X. Mining Association Rules from Tabular Data Guided by Maximal Frequent Itemsets. In: Chu, W., Young Lin, T. (eds) Foundations and Advances in Data Mining. Studies in Fuzziness and Soft Computing, vol 180. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11362197_7
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DOI: https://doi.org/10.1007/11362197_7
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Publisher Name: Springer, Berlin, Heidelberg
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