Abstract
This paper proposes a bitmap representation approach for modifying the erasable-itemset mining algorithm to increase its efficiency. The proposed approach uses the bitmap concept to save processing time. Through experimental evaluation, simulation datasets were used to compare the traditional erasable itemset mining and the proposed approach under various experimental conditions.
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References
Agrawal, R., Srikant, R.: Fast algorithm for mining association rules. In: The 20th International Conference on Very Large Data Bases, pp. 487–499 (1994)
Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large database. In: The ACM SIGMOD International Conference on Management of Data, pp. 207–216 (1993)
Lee, G., Yun, U., Ryang, H.: Mining weighted wrasable patterns by using underestimated constraint-based pruning technique. J. Intell. Fuzzy Syst. 28(3), 1145–1157 (2015)
Chan, R., Yang, Q., Shen, Y.D.: Mining high utility itemsets. In: The 3rd IEEE International Conference on Data Mining, pp. 19–26 (2003)
Yen, S.J., Lee, Y.S.: Mining high utility quantitative association rules. In: The 9th International Conference on Data Warehousing and Knowledge Discovery, pp. 283–292 (2007)
Liu, Y., Liao, W.K., Choudhary, A.: A fast high utility itemsets mining algorithm. In: The 1st International Workshop on Utility-Based Data Mining, pp. 90–99 (2005)
Liu, Y., Li, J., Liao, W.K., Choudhary, A., Shi, Y.: High utility itemsets mining. Int. J. Inf. Technol. Decis. Mak. 09(06), 905–934 (2010)
Deng, Z.H., Fang, G.D., Wang, Z.H., Xu, X.R.: Mining erasable itemsets. In: The 8th International Conference on Machine Learning and Cybernetics, pp. 67–73 (2009)
Deng, Z.H., Xu, X.R.: Fast mining erasable itemsets using nc_sets. Expert Syst. Appl. 39(4), 4453–4463 (2012)
Deng, Z.H., Xu, X.R.: An efficient algorithm for mining erasable itemsets. In: The 6th International Conference on Advanced Data Mining and Applications, pp. 214–225 (2010)
Lan, G.C., Hong, T.P., Lin, Y.H., Wang, S.L.: Fuzzy utility mining with upper-bound measure. Appl. Soft Comput. 30, 767–777 (2015)
Chen, C.H., Li, A.F., Lee, Y.C.: Actionable high-coherent-utility fuzzy itemset mining. Soft. Comput. 18(12), 2413–2424 (2014)
Wang, C.M., Chen, S.H., Huang, Y.F.: A fuzzy approach for mining high utility quantitative itemset. In: The IEEE International Conference on Fuzzy Systems, pp. 1909–1913 (2009)
Lin, C.W., Hong, T.P.: A new mining approach for uncertain databases using CUFP trees. Expert Syst. Appl. 39(4), 4084–4093 (2012)
Hong, T.P., Lin, C.W., Lin, T.C., Chen, Y.F., Pan, S.T.: Integration of multiple fuzzy FP-trees. In: Intelligent Information and Database Systems, pp. 330–337 (2012)
IBM Quest Data Mining Projection, Quest synthetic data generation code (1996). http://www.almaden.ibm.com/cs/quest/syndata.htm
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Huang, WM., Hong, TP., Lan, GC., Chiang, MC., Lin, J.CW. (2018). Mining Erasable Itemsets Using Bitmap Representation. In: Lin, JW., Pan, JS., Chu, SC., Chen, CM. (eds) Genetic and Evolutionary Computing. ICGEC 2017. Advances in Intelligent Systems and Computing, vol 579. Springer, Singapore. https://doi.org/10.1007/978-981-10-6487-6_5
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DOI: https://doi.org/10.1007/978-981-10-6487-6_5
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