Cited By
View all- Tong YChen LShe J(2015)Mining Frequent Itemsets in Correlated Uncertain DatabasesJournal of Computer Science and Technology10.1007/s11390-015-1555-930:4(696-712)Online publication date: 8-Jul-2015
DAGger: clustering correlated uncertain data (to predict asset failure in energy networks)
Pages 1504 - 1507
Abstract
DAGger is a clustering algorithm for uncertain data. In contrast to prior work, DAGger can work on arbitrarily correlated data and can compute both exact and approximate clusterings with error guarantees.
We demonstrate DAGger using a real-world scenario in which partial discharge data from UK Power Networks is clustered to predict asset failure in the energy network.
References
[1]
C. C. Aggarwal. Managing and Mining Uncertain Data, volume 35 of Advances in Database Systems. Kluwer, 2009.
[2]
C. M. Bishop. Pattern Recognition and Machine Learning. Springer-Verlag, 2006.
[3]
R. Cheng and S. Prabhakar. Managing uncertainty in sensor database. SIGMOD Rec., 32:41--46, 12 2003.
[4]
A. Deshpande, C. Guestrin, S. Madden, J. Hellerstein, and W. Hong. Model-driven data acquisition in sensor networks. In VLDB, 2004.
[5]
F. Gullo, G. Ponti, and A. Tagarelli. Clustering uncertain data via k-medoids. In SUM, 2008.
[6]
A. Jha and D. Suciu. Probabilistic databases with markoviews. In VLDB, 2012.
[7]
J. B. MacQueen. Some methods for classification and analysis of multivariate observations. In Berkeley Symp. on Mathematical Statistics and Probability, 1967.
[8]
M. Michel. Innovative asset management and targeted investments using on-line partial discharge monitoring and mapping techniques. In CED, 2007.
[9]
M. Michel and C. Eastham. Improving the management of MV underground cable circuits using automated on-line cable partial discharge mapping. In CEED, 2011.
[10]
B. Qin, Y. Xia, R. Sathyesh, S. Prabhakar, and Y. Tu. urule: A rule-based classification system for uncertain data. In ICDM Workshops, 2010.
[11]
M. Richardson and P. Domingos. Markov logic networks. Machine learning, 62(1):107--136, 2006.
[12]
S. Sathe, H. Jeung, and K. Aberer. Creating probabilistic databases from imprecise time-series data. In ICDE, 2011.
[13]
D. Suciu, D. Olteanu, C. Ré, and C. Koch. Probabilistic Databases. Morgan & Claypool Publishers, 2011.
[14]
L. Sun, R. Cheng, D. W. Cheung, and J. Cheng. Mining uncertain data with probabilistic guarantees. In KDD, 2010.
[15]
G. Taylor, D. Wallom, S. Grenard, A. Yunta Huete, and C. J. Axon. Recent developments towards novel high performance computing and communications solutions for smart distribution network operation. In ISGT, 2011.
Index Terms
- DAGger: clustering correlated uncertain data (to predict asset failure in energy networks)
Recommendations
Fuzzy kernel K-medoids clustering algorithm for uncertain data objects
AbstractMost data mining algorithms are designed for traditional type of data objects which are referred to as certain data objects. Certain data objects contain no uncertainty information and are represented by a single point. Capturing uncertainty can ...
An information-theoretic approach to hierarchical clustering of uncertain data
Uncertain data clustering has become central in mining data whose observed representation is naturally affected by imprecision, staling, or randomness that is implicit when storing this data from real-word sources. Most existing methods for uncertain ...
Approximation algorithms for clustering uncertain data
PODS '08: Proceedings of the twenty-seventh ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systemsThere is an increasing quantity of data with uncertainty arising from applications such as sensor network measurements, record linkage, and as output of mining algorithms. This uncertainty is typically formalized as probability density functions over ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
August 2012
1616 pages
ISBN:9781450314626
DOI:10.1145/2339530
- General Chair:
- Qiang Yang,
- Program Chairs:
- Deepak Agarwal,
- Jian Pei
Copyright © 2012 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 12 August 2012
Check for updates
Author Tags
Qualifiers
- Demonstration
Conference
KDD '12: The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
August 12 - 16, 2012
Beijing, China
Acceptance Rates
Overall Acceptance Rate 1,133 of 8,635 submissions, 13%
Upcoming Conference
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 228Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 16 Feb 2025
Other Metrics
Citations
Cited By
View all- Tong YChen LShe J(2015)Mining Frequent Itemsets in Correlated Uncertain DatabasesJournal of Computer Science and Technology10.1007/s11390-015-1555-930:4(696-712)Online publication date: 8-Jul-2015
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in