Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1109/ICDM.2010.42guideproceedingsArticle/Chapter ViewAbstractPublication PagesConference Proceedingsacm-pubtype
Article

Approximation of Frequentness Probability of Itemsets in Uncertain Data

Published: 13 December 2010 Publication History

Abstract

Mining frequent item sets from transactional datasets is a well known problem with good algorithmic solutions. Most of these algorithms assume that the input data is free from errors. Real data, however, is often affected by noise. Such noise can be represented by uncertain datasets in which each item has an existence probability. Recently, Bernecker et al. (2009) proposed the frequentness probability, i.e., the probability that a given item set is frequent, to select item sets in an uncertain database. A dynamic programming approach to evaluate this measure was given as well. We argue, however, that for the setting of Bernecker et al. (2009), that assumes independence between the items, already well-known statistical tools exist. We show how the frequentness probability can be approximated extremely accurately using a form of the central limit theorem. We experimentally evaluated our approximation and compared it to the dynamic programming approach. The evaluation shows that our approximation method is extremely accurate even for very small databases while at the same time it has much lower memory overhead and computation time.

Cited By

View all

Recommendations

Comments

Information & Contributors

Information

Published In

cover image Guide Proceedings
ICDM '10: Proceedings of the 2010 IEEE International Conference on Data Mining
December 2010
1215 pages
ISBN:9780769542560

Publisher

IEEE Computer Society

United States

Publication History

Published: 13 December 2010

Qualifiers

  • Article

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 09 Nov 2024

Other Metrics

Citations

Cited By

View all
  • (2016)Effective algorithms for vertical mining probabilistic frequent patterns in uncertain mobile environmentsInternational Journal of Ad Hoc and Ubiquitous Computing10.1504/IJAHUC.2016.07926323:3/4(137-151)Online publication date: 1-Jan-2016
  • (2016)New Algorithm for Frequent Itemsets Mining from Evidential Data StreamsProcedia Computer Science10.1016/j.procs.2016.08.24696:C(645-653)Online publication date: 1-Oct-2016
  • (2016)Tracking frequent items over distributed probabilistic dataWorld Wide Web10.1007/s11280-015-0341-519:4(579-604)Online publication date: 1-Jul-2016
  • (2016)Mining significant association rules from uncertain dataData Mining and Knowledge Discovery10.1007/s10618-015-0446-630:4(928-963)Online publication date: 1-Jul-2016
  • (2015)Geo-Social Co-location MiningSecond International ACM Workshop on Managing and Mining Enriched Geo-Spatial Data10.1145/2786006.2786010(19-24)Online publication date: 31-May-2015
  • (2013)Summarizing probabilistic frequent patternsProceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining10.1145/2487575.2487618(527-535)Online publication date: 11-Aug-2013
  • (2013)Mining frequent serial episodes over uncertain sequence dataProceedings of the 16th International Conference on Extending Database Technology10.1145/2452376.2452403(215-226)Online publication date: 18-Mar-2013
  • (2013)FARPInformation Sciences: an International Journal10.1016/j.ins.2013.02.010237(242-260)Online publication date: 1-Jul-2013
  • (2012)Mining frequent itemsets over uncertain databasesProceedings of the VLDB Endowment10.14778/2350229.23502775:11(1650-1661)Online publication date: 1-Jul-2012
  • (2012)UFIMTProceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining10.1145/2339530.2339767(1508-1511)Online publication date: 12-Aug-2012
  • Show More Cited By

View Options

View options

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media