Abstract
Weighted Frequent Itemset Mining (WFIM) has been proposed as an alternative to frequent itemset mining that considers not only the frequency of items but also their relative importance. However, some limitations of WFIM make it unrealistic in many real-world applications. In this paper, we present a new type of knowledge called Recent High Expected Weighted Itemset (RHEWI) to consider the recency, weight and uncertainty of desired patterns, thus more up-to-date and relevant results can be provided to the users. A projection-based algorithm named RHEWI-P is presented to mine RHEWIs based on a novel upper-bound downward closure (UBDC) property. An improved algorithm named RHEWI-PS is further proposed to introduce a sorted upper-bound downward closure (SUBDC) property for pruning unpromising candidates. An experimental evaluation against the state-of-the-art HEWI-Uapriori algorithm is carried on both real-world and synthetic datasets, and the results show that the proposed algorithms are highly efficient and acceptable to mine the required information.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Frequent itemset mining dataset repository. http://fimi.ua.ac.be/data/
Aggarwal, C.C., Yu, P.S.: A survey of uncertain data algorithms and applications. IEEE Trans. Knowl. Data Eng. 21(5), 609–623 (2009)
Agrawal, R., Imielinski, T., Swami, A.: Database mining: a performance perspective. IEEE Trans. Knowl. Data Eng. 5, 914–925 (1993)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: The International Conference on Very Large Data, Bases, pp. 487–499 (1994)
Agrawal, R., Srikant, R.: Quest synthetic data generator. http://www.Almaden.ibm.com/cs/quest/syndata.html
Agrawal, R., Srikant, R.: Mining sequential patterns. In: The International Conference on Data, Engineering, pp. 3–14 (1995)
Cai, C.H., Fu, A.W.C., Kwong, W.W.: Mining association rules with weighted items. In: International Database Engineering and Applications Symposium, pp. 68–77 (1998)
Cagliero, L., Garza, P.: Infrequent weighted itemset mining using frequent pattern growth. IEEE Trans. Knowl. Data Eng. 26(4), 903–915 (2014)
Chen, M.S., Han, J., Yu, P.S.: Data mining: an overview from a database perspective. IEEE Trans. Knowl. Data Eng. 8(6), 866–883 (1996)
Baralis, E., Cagliero, L., Fiori, A., Garza, P.: MWI-Sum: a multilingual summarizer based on frequent weighted itemsets. ACM Trans. Inf. Syst. 34(1), 5 (2015)
Lin, J.C.W., Gan, W., Fournier-Viger, P., Hong, T.P.: RWFIM: recent weighted-frequent itemsets mining. Eng. Appl. Artif. Intell. 45, 18–32 (2015)
Lin, J.C.W., Gan, W., Fournier-Viger, P., Hong, T.P., Tseng, V.S.: Weighted frequent itemset mining over uncertain databases. Appl. Intell. 41(1), 232–250 (2016)
Lin, J.C.-W., Gan, W., Fournier-Viger, P., Hong, T.-P.: Efficient mining of weighted frequent itemsets in uncertain databases. In: Perner, P. (ed.) MLDM 2016. LNCS, vol. 9729, pp. 236–250. Springer, Heidelberg (2016). doi:10.1007/978-3-319-41920-6_18
Sun, K., Bai, F.: Mining weighted association rules without preassigned weights. IEEE Trans. Knowl. Data Eng. 20(4), 489–495 (2008)
Tao, F., Murtagh, F., Farid, M.: Weighted association rule mining using weighted support and significance framework. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 661–666 (2003)
Wang, W., Yang, J., Yu, P.S.: Efficient mining of weighted association rules (WAR). In: ACM SIGKDD Intern. Conf. on Knowledge Discovery and Data Mining, pp. 270–274 (2000)
Yun, U., Leggett, J.: WFIM: weighted frequent itemset mining with a weight range and a minimum weight. In: SIAM International Conference on Data Mining, pp. 636–640 (2005)
Acknowledgment
This research was partially supported by the National Natural Science Foundation of China (NSFC) under Grant No.61503092, and by the Tencent Project under grant CCF-TencentRAGR20140114.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Gan, W., Lin, J.CW., Fournier-Viger, P., Chao, HC. (2016). Mining Recent High Expected Weighted Itemsets from Uncertain Databases. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9931. Springer, Cham. https://doi.org/10.1007/978-3-319-45814-4_47
Download citation
DOI: https://doi.org/10.1007/978-3-319-45814-4_47
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-45813-7
Online ISBN: 978-3-319-45814-4
eBook Packages: Computer ScienceComputer Science (R0)