Abstract
Databases are updated continuously with increments and re-running the frequent itemset mining algorithms with every update is inefficient. Studies addressing incremental update problem generally propose incremental itemset mining methods based on Apriori and FP-Growth algorithms. Besides inheriting the disadvantages of base algorithms, incremental itemset mining has challenges such as handling i) increments without re-running the algorithm, ii) support changes, iii) new items and iv) addition/deletions in increments. In this paper, we focus on the solution of incremental update problem by proposing the Incremental Matrix Apriori Algorithm. It scans only new transactions, allows the change of minimum support and handles new items in the increments. The base algorithm Matrix Apriori works without candidate generation, scans database only twice and brings additional advantages. Performance studies show that Incremental Matrix Apriori provides speed-up between 41% and 92% while increment size is varied between 5% and 100%.
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References
Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules Between Sets of Items in Large Databases. ACM SIGMOD Record 22(2), 207–216 (1993)
Han, J., Kamber, M.: Data mining: Concepts and Techniques. The Morgan Kaufmann Series in Data Management Systems. Elsevier, San Francisco (2006)
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules in Large Databases. In: 20th International Conference on Very Large Data Bases, pp. 487–499. Morgan Kaufmann Publishers Inc., San Francisco (1994)
Han, J., Pei, J., Yin, Y.: Mining Frequent Patterns without Candidate Generation. ACM SIGMOD Rec. 29(2), 1–12 (2000)
Pavon, J., Viana, S., Gomez, S.: Matrix Apriori: Speeding Up the Search for Frequent Patterns. In: 24th IASTED International Conference on Database and Applications, pp. 75–82. ACTA Press, Anaheim (2006)
Yıldız, B., Ergenç, B.: Comparison of Two Association Rule Mining Algorithms without Candidate Generation. In: 10th IASTED International Conference on Artificial Intelligence and Applications, Innsbruck (2010)
Cheung, D., Han, J., Ng, V., Wong, C.: Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique. In: 12th International Conference on Data Engineering, pp. 106–114. IEEE Computer Society, Washington (1996)
Woon, Y.K., Ng, W.K., Das, A.: Fast online dynamic association rule mining. In: 2nd International Conference, vol. 1, pp. 278–287 (2001)
Amornchewin, R., Kreesuradej, W.: Incremental Association Rule Mining Using Promising Frequent Itemset Algorithm. In: 6th International Conference on Information, Communications & Signal Processing, pp. 1–5 (2007)
Hong, T.P., Lin, C.W., Wu, Y.L.: Incrementally Fast Updated Frequent Pattern Trees. Expert Syst. Appl. 34(4), 2424–2435 (2008)
Muhaimenul, A.R., Barker, K.: Alternative Method for Incrementally Constructing the FP-tree. In: Chountas, P., Petrounias, I., Kacprzyk, J. (eds.) Intelligent Techniques and Tools for Novel System Architectures. SCI, vol. 109, pp. 361–377. Springer, Heidelberg (2008)
Jin, R., Agrawal, G.: An Algorithm for In-Core Frequent Itemset Mining on Streaming Data. In: 5th IEEE International Conference on Data Mining, pp. 210–217. IEEE Computer Society, Washington (2005)
Calders, T., Dexters, N., Goethals, B.: Mining Frequent Itemsets in a Stream. In: 7th IEEE Conference on Data Mining, pp. 83–92. IEEE Computer Society, Washington (2007)
Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G.J., Ng, A., Liu, B., Yu, P.S., Zhou, Z.H., Steinbach, M., Hand, D.J., Steinberg, D.: Top 10 Algorithms in Data Mining. Knowl. Inf. Syst. 14(1), 1–37 (2007)
Kotsiantis, S., Kanellopoulos, D.: Association Rules Mining: A Recent Overview Basic Concepts & Basic Association Rules Algorithms. Science 32(1), 71–82 (2006)
Park, J.S., Chen, M.S., Yu, P.S.: An Effective Hash-Based Algorithm for Mining Association Rules. SIGMOD Rec. 24(2), 175–186 (1995)
Cheung, D.W.L., Lee, S.D., Kao, B.: A General Incremental Technique for Maintaining Discovered Association Rules. In: 5th International Conference on Database Systems for Advanced Applications, pp. 185–194. World Scientific Press (1997)
Lin, C.-W., Hong, T.-P., Lu, W.-H., Chien, B.-C.: Incremental Mining with Prelarge Trees. In: Nguyen, N.T., Borzemski, L., Grzech, A., Ali, M. (eds.) IEA/AIE 2008. LNCS (LNAI), vol. 5027, pp. 169–178. Springer, Heidelberg (2008)
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Oguz, D., Ergenc, B. (2012). Incremental Itemset Mining Based on Matrix Apriori Algorithm. In: Cuzzocrea, A., Dayal, U. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2012. Lecture Notes in Computer Science, vol 7448. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32584-7_16
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DOI: https://doi.org/10.1007/978-3-642-32584-7_16
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