Article

Efficient pattern mining of uncertain data with sampling

Authors:

PAKDD'10: Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I

Pages 480 - 487

https://doi.org/10.1007/978-3-642-13657-3_51

Published: 21 June 2010 Publication History

Publisher Site

Abstract

Mining frequent itemsets from transactional datasets is a well known problem with good algorithmic solutions. In the case of uncertain data, however, several new techniques have been proposed. Unfortunately, these proposals often suffer when a lot of items occur with many different probabilities. Here we propose an approach based on sampling by instantiating “possible worlds” of the uncertain data, on which we subsequently run optimized frequent itemset mining algorithms. As such we gain efficiency at a surprisingly low loss in accuracy. These is confirmed by a statistical and an empirical evaluation on real and synthetic data.

References

[1]

Aggarwal, C.C., Li, Y.,Wang, J., Wang, J.: Frequent pattern mining with uncertain data. In: Proc. of KDD 2009, pp. 29-38. ACM, New York (2009).

Digital Library

Google Scholar

[2]

Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proc. of VLDB 1994, pp. 487-499. Morgan Kaufmann Publishers Inc, San Francisco (1994).

Digital Library

Google Scholar

[3]

Chui, C.K., Kao, B.: A decremental approach for mining frequent itemsets from uncertain data. In: Washio, et al {9}, pp. 64-75.

Digital Library

Google Scholar

[4]

Chui, C.K., Kao, B., Hung, E.: Mining frequent itemsets from uncertain data. In: Zhou, Z.-H., Li, H., Yang, Q. (eds.) PAKDD 2007. LNCS (LNAI), vol. 4426, pp. 47-58. Springer, Heidelberg (2007).

Digital Library

Google Scholar

[5]

Goethals, B.: Frequent set mining. In: The Data Mining and Knowledge Discovery Handbook, ch. 17, pp. 377-397. Springer, Heidelberg (2005).

Crossref

Google Scholar

[6]

Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Min. Knowl. Discov. 8(1), 53- 87 (2004).

Digital Library

Google Scholar

[7]

Leung, C.K.-S., Mateo, M.A.F., Brajczuk, D.A.: A tree-based approach for frequent pattern mining from uncertain data. In: Washio, et al {9}, pp. 653-661.

Digital Library

Google Scholar

[8]

Pei, J., Han, J., Lu, H., Nishio, S., Tang, S., Yang, D.: H-mine: Hyper-structure mining of frequent patterns in large databases. In: Proc. of ICDM 2001, Washington, DC, USA, pp. 441-448. IEEE Computer Society, Los Alamitos (2001).

Digital Library

Google Scholar

[9]

Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.): PAKDD 2008. LNCS (LNAI), vol. 5012. Springer, Heidelberg (2008).

Google Scholar

[10]

Zaki, M.J., Gouda, K.: Fast vertical mining using diffsets. In: Getoor, L., Senator, T.E., Domingos, P., Faloutsos, C. (eds.) Proc. of KDD 2003, pp. 326-335. ACM, New York (2003).

Digital Library

Google Scholar

[11]

Zaki, M.J., Parthasarathy, S., Ogihara, M., Li, W.: New algorithms for fast discovery of association rules. In: Proc. of KDD 1997, pp. 283-286 (1997).

Google Scholar

Cited By

View all

Fasihy HShahraki M(2018)Incremental mining maximal frequent patterns from univariate uncertain dataKnowledge-Based Systems10.1016/j.knosys.2018.04.001152:C(40-50)Online publication date: 15-Jul-2018
https://dl.acm.org/doi/10.1016/j.knosys.2018.04.001
Tong YZhang XChen L(2016)Tracking frequent items over distributed probabilistic dataWorld Wide Web10.1007/s11280-015-0341-519:4(579-604)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1007/s11280-015-0341-5
Pei BZhao SChen HZhou XChen D(2013)FARPInformation Sciences: an International Journal10.1016/j.ins.2013.02.010237(242-260)Online publication date: 1-Jul-2013
https://dl.acm.org/doi/10.1016/j.ins.2013.02.010
Show More Cited By

Index Terms

Efficient pattern mining of uncertain data with sampling
1. Information systems
  1. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

Recommendations

Mining uncertain data

As an important data mining and knowledge discovery task, association rule mining searches for implicit, previously unknown, and potentially useful pieces of information—in the form of rules revealing associative relationships—that are embedded in the ...
Efficient algorithms for mining constrained frequent patterns from uncertain data
U '09: Proceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data

Mining of frequent patterns is one of the popular knowledge discovery and data mining (KDD) tasks. It also plays an essential role in the mining of many other patterns such as correlation, sequences, and association rules. Hence, it has been the subject ...
Efficient algorithms for mining high-utility itemsets in uncertain databases

High-utility itemset mining (HUIM) is a useful set of techniques for discovering patterns in transaction databases, which considers both quantity and profit of items. However, most algorithms for mining high-utility itemsets (HUIs) assume that the ...

Comments

Information & Contributors

Information

Published In

PAKDD'10: Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I

June 2010

499 pages

ISBN:3642136567

Editors:
Mohammed J. Zaki
Computer Science Department, Rensselaer Polytechnic Institute
,
Jeffrey Xu Yu
Computer Science Department, The Chinese University of Hong Kong, China
,
B. Ravindran
Computer Science Department, IIT Madras, Chennai, India
,
Vikram Pudi
Computer Science Department, IIIT, Hyderabad, India

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 21 June 2010

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

12
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 03 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Fasihy HShahraki M(2018)Incremental mining maximal frequent patterns from univariate uncertain dataKnowledge-Based Systems10.1016/j.knosys.2018.04.001152:C(40-50)Online publication date: 15-Jul-2018
https://dl.acm.org/doi/10.1016/j.knosys.2018.04.001
Tong YZhang XChen L(2016)Tracking frequent items over distributed probabilistic dataWorld Wide Web10.1007/s11280-015-0341-519:4(579-604)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1007/s11280-015-0341-5
Pei BZhao SChen HZhou XChen D(2013)FARPInformation Sciences: an International Journal10.1016/j.ins.2013.02.010237(242-260)Online publication date: 1-Jul-2013
https://dl.acm.org/doi/10.1016/j.ins.2013.02.010
Jiang FLeung C(2013)Stream Mining of Frequent Patterns from Delayed Batches of Uncertain DataProceedings of the 15th International Conference on Data Warehousing and Knowledge Discovery - Volume 805710.1007/978-3-642-40131-2_18(209-221)Online publication date: 26-Aug-2013
https://dl.acm.org/doi/10.1007/978-3-642-40131-2_18
de Carvalho JRuiz D(2013)Discovering frequent itemsets on uncertain dataProceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition10.1007/978-3-642-39712-7_30(390-404)Online publication date: 19-Jul-2013
https://dl.acm.org/doi/10.1007/978-3-642-39712-7_30
Tong YChen LCheng YYu P(2012)Mining frequent itemsets over uncertain databasesProceedings of the VLDB Endowment10.14778/2350229.23502775:11(1650-1661)Online publication date: 1-Jul-2012
https://dl.acm.org/doi/10.14778/2350229.2350277
Leung CTanbeer SBudhia BZacharias LDesai BPokorny JBernardino J(2012)Mining probabilistic datasets verticallyProceedings of the 16th International Database Engineering & Applications Sysmposium10.1145/2351476.2351500(199-204)Online publication date: 8-Aug-2012
https://dl.acm.org/doi/10.1145/2351476.2351500
Leung CTanbeer S(2012)Fast tree-based mining of frequent itemsets from uncertain dataProceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I10.1007/978-3-642-29038-1_21(272-287)Online publication date: 15-Apr-2012
https://dl.acm.org/doi/10.1007/978-3-642-29038-1_21
Borgelt C(2012)Frequent item set miningWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery10.1002/widm.10742:6(437-456)Online publication date: 1-Nov-2012
https://dl.acm.org/doi/10.1002/widm.1074
Borgelt CKötter T(2011)Mining fault-tolerant item sets using subset size occurrence distributionsProceedings of the 10th international conference on Advances in intelligent data analysis X10.5555/2075337.2075346(43-54)Online publication date: 29-Oct-2011
https://dl.acm.org/doi/10.5555/2075337.2075346
Show More Cited By

Abstract

References

Cited By

Index Terms

Recommendations

Mining uncertain data

Efficient algorithms for mining constrained frequent patterns from uncertain data

Efficient algorithms for mining high-utility itemsets in uncertain databases

Comments

Information

Published In

Sponsors

Publisher

Publication History

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations