Abstract
There are many stand-alone algorithms to mine different types of patterns in traditional databases. However, to effectively and efficiently mine databases with more complex and large data tables is still a growing challenge in data mining. The nature of data streams makes streaming techniques a promising way to handle large amounts of data, since their main ideas are to avoid multiple scans and optimize memory usage. In this paper we propose in detail an algorithm for finding frequent patterns in large databases following a star schema, based on streaming techniques. It is able to mine traditional star schemas, as well as stars with degenerate dimensions. It is able to aggregate the rows in the fact table that relate to the same business fact, and therefore find patterns at the right business level. Experimental results show that the algorithm is accurate and performs better than the traditional approach.
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References
Crestana-Jensen, V., Soparkar, N.: Frequent itemset counting across multiple tables. In: PADKK 2000: Proc. of the 4th Pacific-Asia Conf. on Knowl. Discovery and Data Mining, Current Issues and New Applications, London, pp. 49–61 (2000)
Dehaspe, L., Raedt, L.: Mining Association Rules in Multiple Relations. In: Džeroski, S., Lavrač, N. (eds.) ILP 1997. LNCS, vol. 1297, pp. 125–132. Springer, Heidelberg (1997)
Fumarola, F., Ciampi, A., Appice, A., Malerba, D.: A Sliding Window Algorithm for Relational Frequent Patterns Mining from Data Streams. In: Gama, J., Costa, V.S., Jorge, A.M., Brazdil, P.B. (eds.) DS 2009. LNCS, vol. 5808, pp. 385–392. Springer, Heidelberg (2009)
Giannella, C., Han, J., Pei, J., Yan, X., Yu, P.S.: Mining frequent patterns in data streams at multiple time granularities: Next generation data mining (2003)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proc. of the 2000 ACM SIGMOD, pp. 1–12. ACM, New York (2000)
Hou, W., Yang, B., Xie, Y., Wu, C.: Mining multi-relational frequent patterns in data streams. In: BIFE 2009: Proc. of the Second Intern. Conf. on Business Intelligence and Financial Engineering, pp. 205–209 (2009)
Kimball, R., Ross, M.: The Data warehouse Toolkit, 2nd edn. John Wiley & Sons, Inc., New York (2002)
Liu, H., Lin, Y., Han, J.: Methods for mining frequent items in data streams: an overview. Knowl. Inf. Syst. 26, 1–30 (2011)
Ng, E., Fu, A., Wang, K.: Mining association rules from stars. In: ICDM 2002: Proc. of the 2002 IEEE Intern. Conf. on DM, Japan, pp. 322–329. IEEE (2002)
Silva, A., Antunes, C.: Pattern Mining on Stars with FP-Growth. In: Torra, V., Narukawa, Y., Daumas, M. (eds.) MDAI 2010. LNCS, vol. 6408, pp. 175–186. Springer, Heidelberg (2010)
Silva, A., Antunes, C.: Mining Patterns from Large Star Schemas Based on Streaming Algorithms. In: Lee, R. (ed.) Computer and Information Science 2012. SCI, vol. 429, pp. 139–150. Springer, Heidelberg (2012)
Xu, L.-J., Xie, K.-L.: A novel algorithm for frequent itemset mining in data warehouses. Journal of Zhejiang University - Science A 7(2), 216–224 (2006)
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Silva, A., Antunes, C. (2012). Finding Patterns in Large Star Schemas at the Right Aggregation Level. In: Torra, V., Narukawa, Y., López, B., Villaret, M. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2012. Lecture Notes in Computer Science(), vol 7647. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34620-0_30
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DOI: https://doi.org/10.1007/978-3-642-34620-0_30
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