Abstract
Imputation of missing values is an important data mining task for improving the quality of data mining results. The imputation based on similar records is generally more accurate than the imputation based on all records of adata set. Therefore, in this paper we present a novel algorithm called kDMI that employs two levels of horizontal partitioning (based on a decision tree and k-NNalgorithm) of a data set, in order to find the records that are very similar to the one with missing value/s. Additionally, it uses a novel approach to automatically find the value of k for each record. We evaluate the performance of kDMI over three high quality existing methods on two real data sets in terms of four evaluation criteria. Our initial experimental results, including 95% confidence interval analysis and statistical t-test analysis, indicate the superiority of kDMI over the existing methods.
Original language | English |
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Title of host publication | Lecture Notes in Artificial Intelligence |
Subtitle of host publication | Subseries of Lecture Notes in Computer Science |
Place of Publication | Berlin |
Publisher | Springer-Verlag London Ltd. |
Pages | 250-263 |
Number of pages | 14 |
Volume | 8347 |
ISBN (Electronic) | 9783642539176 |
ISBN (Print) | 9783642539169 |
DOIs | |
Publication status | Published - 2013 |
Event | The 9th International Conference on Advanced Data Mining and Applications (ADMA 2013) - Zhejiang University, Hangzhou, China Duration: 14 Dec 2013 → 16 Dec 2013 https://web.archive.org/web/20130623022301/http://www.adma2013.org:80/ |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer Verlag |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | The 9th International Conference on Advanced Data Mining and Applications (ADMA 2013) |
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Country/Territory | China |
City | Hangzhou |
Period | 14/12/13 → 16/12/13 |
Other | The conference aims at bringing together the experts on data mining from around the world, and providing a leading international forum for the dissemination of original research findings in data mining, spanning applications, algorithms, software and systems, as well as different applied disciplines with potential in data mining. ADMA 2013 will promote the same close interaction among practitioners and researchers. Published papers will go through a full peer review process. |
Internet address |