Difference between revisions of "NTDM 2008"
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{{Event | {{Event | ||
| + | | Acronym = NTDM 2008 | ||
| + | | Title = PAKDD08 workshop on Noise Tolerant Mining in Databases | ||
| + | | Type = Workshop | ||
| + | | Series = | ||
| Field = Data mining | | Field = Data mining | ||
| + | | Homepage = www.ntu.edu.sg/home/asvivek/ntmd | ||
| + | | Start date = May 20, 2008 | ||
| + | | End date = May 23, 2008 | ||
| + | | City= Osaka | ||
| + | | State = | ||
| + | | Country = Japan | ||
| Abstract deadline = | | Abstract deadline = | ||
| Submission deadline = Jan 6, 2008 | | Submission deadline = Jan 6, 2008 | ||
| Notification = | | Notification = | ||
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Revision as of 16:06, 3 November 2021
Event Rating
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List of all ratings can be found at NTDM 2008/rating
| NTDM 2008 | |
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PAKDD08 workshop on Noise Tolerant Mining in Databases
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| Dates | May 20, 2008 (iCal) - May 23, 2008 |
| Homepage: | www.ntu.edu.sg/home/asvivek/ntmd |
| Location | |
| Location: | Osaka, Japan |
| Important dates | |
| Submissions: | Jan 6, 2008 |
| Table of Contents | |
Transactional data is ubiquituous in many real world applications. While market-basket, protein-taxa, gene expression, etc. naturally present themselves as transactional data, others like streaming/time-series data, text data, protein-protein interactions, etc. can also be modeled as transactional data. Transactional databases are typically large and have high dimensionality, and may also be affected by noise. Noise may arise due to missing values, erroneous readings or uncertainty in data. Depending on the domain, the noise may be either known or unknown (both in distribution and in amount). In this workshop, we focus on common mining problems: Frequent itemset mining, Association Rule Mining, Subspace clustering and Co-clustering, on transactional databases in the presence of noise. In such problems, noise can lead to incorrect mining results and also degrade mining performance. So it is critical to develop noise aware mining techniques which are robust and efficient. Besides developing innovative techniques, it is also important to validate them using statistical/domain knowledge or by real-world case studies.
This CfP was obtained from WikiCFP