Abstract
Streams of data can be continuously generated by sensors in various real-life applications such as environment surveillance. Partially due to the inherited limitation of the sensors, data in these streams can be uncertain. To discover useful knowledge in the form of frequent patterns from streams of uncertain data, a few algorithms have been developed. They mostly use the sliding window model for processing and mining data streams. However, for some applications, other stream processing models such as the time-fading model are more appropriate. Moreover, batches of data in the stream may be delayed and not arrived in the intended order. In this paper, we propose mining algorithms that use the time-fading model to mine frequent patterns when these batches in the streams of uncertain data were delayed and arrived out of order.
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
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Aggarwal, C.C., Li, Y., Wang, J., Wang, J.: Frequent pattern mining with uncertain data. In: ACM KDD 2009, pp. 29–37 (2009)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: VLDB 1994, pp. 487–499 (1994)
Budhia, B.P., Cuzzocrea, A., Leung, C.K.-S.: Vertical frequent pattern mining from uncertain data. In: KES 2012, pp. 1273–1282 (2012)
Calders, T., Garboni, C., Goethals, B.: Efficient pattern mining of uncertain data with sampling. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds.) PAKDD 2010, Part I. LNCS (LNAI), vol. 6118, pp. 480–487. Springer, Heidelberg (2010)
Ezeife, C.I., Zhang, D.: TidFP: mining frequent patterns in different databases with transaction ID. In: Pedersen, T.B., Mohania, M.K., Tjoa, A.M. (eds.) DaWaK 2009. LNCS, vol. 5691, pp. 125–137. Springer, Heidelberg (2009)
Fariha, A., Ahmed, C.F., Leung, C.K.-S., Abdullah, S.M., Cao, L.: Mining frequent patterns from human interactions in meetings using directed acyclic graph. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013, Part I. LNCS (LNAI), vol. 7818, pp. 38–49. Springer, Heidelberg (2013)
Giannella, C., Han, J., Pei, J., Yan, X., Yu, P.S.: Mining frequent patterns in data streams at multiple time granularities. In: Data Mining: Next Generation Challenges and Future Directions, pp. 105–124 (2004)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: ACM SIGMOD 2000, pp. 1–12 (2000)
Huang, D., Koh, Y.S., Dobbie, G.: Rare pattern mining on data streams. In: Cuzzocrea, A., Dayal, U. (eds.) DaWaK 2012. LNCS, vol. 7448, pp. 303–314. Springer, Heidelberg (2012)
Jiang, N., Gruenwald, L.: Research issues in data stream association rule mining. ACM SIGMOD Record 35(1), 14–19 (2006)
Leung, C.K.-S.: Mining uncertain data. WIREs Data Mining and Knowledge Discover 1(4), 316–329 (2011)
Leung, C.K.-S., Cuzzocrea, A., Jiang, F.: Discovering frequent patterns from uncertain data streams with time-fading and landmark models. In: Hameurlain, A., Küng, J., Wagner, R., Cuzzocrea, A., Dayal, U. (eds.) TLDKS VIII. LNCS, vol. 7790, pp. 174–196. Springer, Heidelberg (2013)
Leung, C.K.-S., Hao, B.: Mining of frequent itemsets from streams of uncertain data. In: IEEE ICDE 2009, pp. 1663–1670 (2009)
Leung, C.K.-S., Hayduk, Y.: Mining frequent patterns from uncertain data with MapReduce for Big Data analytics. In: Meng, W., Feng, L., Bressan, S., Winiwarter, W., Song, W. (eds.) DASFAA 2013, Part I. LNCS, vol. 7825, pp. 440–455. Springer, Heidelberg (2013)
Leung, C.K.-S., Jiang, F.: Frequent pattern mining from time-fading streams of uncertain data. In: Cuzzocrea, A., Dayal, U. (eds.) DaWaK 2011. LNCS, vol. 6862, pp. 252–264. Springer, Heidelberg (2011)
Leung, C.K.-S., Mateo, M.A.F., Brajczuk, D.A.: A tree-based approach for frequent pattern mining from uncertain data. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 653–661. Springer, Heidelberg (2008)
Leung, C.K.-S., Tanbeer, S.K.: Mining popular patterns from transactional databases. In: Cuzzocrea, A., Dayal, U. (eds.) DaWaK 2012. LNCS, vol. 7448, pp. 291–302. Springer, Heidelberg (2012)
Leung, C.K.-S., Tanbeer, S.K.: PUF-tree: A compact tree structure for frequent pattern mining of uncertain data. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013, Part I. LNCS (LNAI), vol. 7818, pp. 13–25. Springer, Heidelberg (2013)
Leung, C.K.-S., Tanbeer, S.K., Budhia, B.P., Zacharias, L.C.: Mining probabilistic datasets vertically. In: IDEAS 2012, pp. 199–204 (2012)
Qu, J.-F., Liu, M.: A fast algorithm for frequent itemset mining using Patricia* structures. In: Cuzzocrea, A., Dayal, U. (eds.) DaWaK 2012. LNCS, vol. 7448, pp. 205–216. Springer, Heidelberg (2012)
Tanbeer, S.K., Leung, C.K.-S.: Finding diverse friends in social networks. In: Ishikawa, Y., Li, J., Wang, W., Zhang, R., Zhang, W. (eds.) APWeb 2013. LNCS, vol. 7808, pp. 301–309. Springer, Heidelberg (2013)
Yu, J.X., Chong, X., Lu, H., Zhou, A.: False positive or false negative: mining frequent itemsets from high speed transactional data streams. In: VLDB 2004, pp. 204–215 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag GmbH Berlin Heidelberg
About this paper
Cite this paper
Jiang, F., Leung, C.KS. (2013). Stream Mining of Frequent Patterns from Delayed Batches of Uncertain Data. In: Bellatreche, L., Mohania, M.K. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2013. Lecture Notes in Computer Science, vol 8057. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40131-2_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-40131-2_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40130-5
Online ISBN: 978-3-642-40131-2
eBook Packages: Computer ScienceComputer Science (R0)