kDMI: A Novel Method for Missing Values Imputation Using Two Levels of Horizontal Partitioning in a Data set

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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 languageEnglish
Title of host publicationLecture Notes in Artificial Intelligence
Subtitle of host publicationSubseries of Lecture Notes in Computer Science
Place of PublicationBerlin
PublisherSpringer-Verlag London Ltd.
Pages250-263
Number of pages14
Volume8347
ISBN (Electronic)9783642539176
ISBN (Print)9783642539169
DOIs
Publication statusPublished - 2013
EventThe 9th International Conference on Advanced Data Mining and Applications (ADMA 2013) - Zhejiang University, Hangzhou, China
Duration: 14 Dec 201316 Dec 2013
https://web.archive.org/web/20130623022301/http://www.adma2013.org:80/

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Verlag
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceThe 9th International Conference on Advanced Data Mining and Applications (ADMA 2013)
Country/TerritoryChina
CityHangzhou
Period14/12/1316/12/13
OtherThe 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.
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