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Knowledge and Information Systems

, Volume 24, Issue 2, pp 221–233 | Cite as

Mining incomplete survey data through classification

  • Hai Wang
  • Shouhong Wang
Regular Paper

Abstract

Data mining with incomplete survey data is an immature subject area. Mining a database with incomplete data, the patterns of missing data as well as the potential implication of these missing data constitute valuable knowledge. This paper presents the conceptual foundations of data mining with incomplete data through classification which is relevant to a specific decision making problem. The proposed technique generally supposes that incomplete data and complete data may come from different sub-populations. The major objective of the proposed technique is to detect the interesting patterns of data missing behavior that are relevant to a specific decision making, instead of estimation of individual missing value. Using this technique, a set of complete data is used to acquire a near-optimal classifier. This classifier provides the prediction reference information for analyzing the incomplete data. The data missing behavior concealed in the missing data is then revealed. Using a real-world survey data set, the paper demonstrates the usefulness of this technique.

Keywords

Data mining Knowledge discovery Incomplete survey data Classification 

Abbreviations

BPNN

Layered back-propagation neural networks

CCR

Correct classification rate

HMDA

Home Mortgage Disclosure Act

LDA

Linear discriminant analysis

MI

Multiple imputation

MSA

Metropolitan Statistical Area

List of symbols

C

Classifier

RC

A set of reference information

RM

A set of classification results for SM k

S

A set of survey data with incomplete data

SC

A data set with complete data

SCTest

Sub-set of SC for test of the classifier

SCTrain

Sub-set of SC for training of the classifier

SM

A set of survey data with missing values

SMk

Data sets with artificial imputation values for the missing values

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Copyright information

© Springer-Verlag London Limited 2009

Authors and Affiliations

  1. 1.Sobey School of BusinessSaint Mary’s UniversityHalifaxCanada
  2. 2.Charlton College of BusinessUniversity of Massachusetts DartmouthDartmouthUSA

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