Abstract
Erasable itemset mining first introduced in 2009 is an interesting variation of pattern mining. The managers can use the erasable itemsets for planning production plan of the factory. Besides the problem of mining erasable itemsets, the problem of mining top-rank-k erasable itemsets is an interesting and practical problem. In this paper, we first propose a new structure, call dPID_List and two theorems associated with it. Then, an improved algorithm for mining top-rank-k erasable itemsets using dPID_List structure is developed. The effectiveness of the proposed method has been demonstrated by comparisons in terms of mining time and memory usage with VM algorithm for three datasets.
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References
Agrawal, R., Imielinski, T., Swami, A.: Database mining: A performance perspective. IEEE Transactions on Knowledge and Data Engineering 5(6), 914–925 (1993)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: VLDB 1994, pp. 487–499 (1994)
Deng, Z.H.: Mining top-rank-k erasable itemsets by PID_lists. International Journal of Intelligent Systems 28(4), 366–379 (2013)
Deng, Z.H., Xu, X.R.: Fast mining erasable itemsets using NC_sets. Expert Systems with Applications 39(4), 4453–4463 (2012)
Deng, Z., Fang, G., Wang, Z., Xu, X.: Mining erasable itemsets. In: ICMLC 2009, pp. 67–73 (2009)
Deng, Z.H., Xu, X.R.: An efficient algorithm for mining erasable itemsets. In: ACDM 2010, pp. 214–225 (2009)
Deng, Z., Xu, X.: Mining top-rank-k erasable itemsets. ICIC Express Letter 2011 5(1), 15–20 (2011)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: SIGMOD 2000, pp. 1–12 (2000)
Le, T., Vo, B., Coenen, F.: An efficient algorithm for mining erasable itemsets using the difference of NC-Sets. In: IEEE SMC 2013, Manchester, UK, pp. 2270–2274 (2013)
Le, T., Vo, B.: MEI: An efficient algorithm for mining erasable itemsets. Engineering Applications of Artificial Intelligence 27(1), 155–166 (2014)
Vo, B., Hong, T.-P., Le, B.: DBV-Miner: A dynamic bit-vector approach for fast mining frequent closed itemsets. Expert Systems with Applications 39(8), 7196–7206 (2012)
Vo, B., Le, T., Coenen, F., Hong, T.-P.: A hybrid approach for mining frequent itemsets. In: IEEE SMC 2013, Manchester, UK, pp. 4647–4651 (2013)
Vo, B., Le, T., Hong, T.-P., Le, B.: Maintenance of a frequent-itemset lattice based on Pre-large concept. In: KSE 2013, Ha Noi, Vietnam, pp. 295–305 (2013)
Zaki, M., Gouda, K.: Fast vertical mining using diffsets. In: SIGKDD 2003, pp. 326–335 (2003)
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Nguyen, G., Le, T., Vo, B., Le, B. (2014). A New Approach for Mining Top-Rank-k Erasable Itemsets. In: Nguyen, N.T., Attachoo, B., Trawiński, B., Somboonviwat, K. (eds) Intelligent Information and Database Systems. ACIIDS 2014. Lecture Notes in Computer Science(), vol 8397. Springer, Cham. https://doi.org/10.1007/978-3-319-05476-6_8
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DOI: https://doi.org/10.1007/978-3-319-05476-6_8
Publisher Name: Springer, Cham
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