A hybrid missing data imputation method for constructing city mobility indices

Sanaz Nikfalazar; Chung-Hsing Yeh; Susan Elizabeth Bedingfield; Hadi Akbarzadeh Khorshidi

doi:10.1007/978-981-13-6661-1_11

A hybrid missing data imputation method for constructing city mobility indices

Sanaz Nikfalazar, Chung-Hsing Yeh, Susan Elizabeth Bedingfield, Hadi Akbarzadeh Khorshidi

Department of Data Science & AI

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

4 Citations (Scopus)

Abstract

An effective missing data imputation method is essential for data mining and knowledge discovery from a comprehensive database with missing values. This paper proposes a new hybrid imputation method to effectively deal with the missing data issue of the Mobility in Cities Database (MCD) to construct city mobility indices. The hybrid method integrates the advantages of decision trees and fuzzy clustering into an iterative algorithm for missing data imputation. Extensive experiments conducted on the MCD and three commonly used datasets demonstrate that the hybrid method outperforms other existing effective imputation methods. With the MCD’s missing values imputed by the hybrid method, and using factor analysis and principal component analysis, this paper constructs city mobility indices for 63 cities in the MCD based on the novel concept of city mobility supply and demand. The city mobility indices constructed under a hierarchical structure of mobility supply and demand indicators represent substantial city mobility knowledge discovered from mining the MCD. The proposed hybrid method represents a significant contribution to missing data imputation research.

Original language	English
Title of host publication	Data Mining - 16th Australasian Conference, AusDM 2018 Bahrurst, NSW, Australia, November 28–30, 2018 Revised Selected Papers
Editors	Rafiqul Islam, Yun Sing Koh, Yanchang Zhao, Graco Warwick, David Stirling, Chang-Tsun Li, Zahidul Islam
Place of Publication	Singapore Singapore
Publisher	Springer
Pages	135-148
Number of pages	14
ISBN (Electronic)	9789811366611
ISBN (Print)	9789811366604
DOIs	https://doi.org/10.1007/978-981-13-6661-1_11
Publication status	Published - 2019
Event	Australasian Data Mining Conference 2018 - Bathurst, Australia Duration: 28 Nov 2018 → 30 Nov 2018 Conference number: 16th https://web.archive.org/web/20181122224709/https://ausdm18.ausdm.org/ https://dblp.org/db/conf/ausdm/ausdm2018.html (Proceedings)

Publication series

Name	Communications in Computer and Information Science
Publisher	Springer
Volume	996
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	Australasian Data Mining Conference 2018
Abbreviated title	AusDM 2018
Country/Territory	Australia
City	Bathurst
Period	28/11/18 → 30/11/18
Internet address	https://web.archive.org/web/20181122224709/https://ausdm18.ausdm.org/ https://dblp.org/db/conf/ausdm/ausdm2018.html (Proceedings)

Keywords

City mobility index
Decision tree
Factor analysis
Iterative fuzzy clustering
Missing data imputation
Principal component analysis

Access to Document

10.1007/978-981-13-6661-1_11

Cite this

Nikfalazar, S., Yeh, C.-H., Bedingfield, S. E., & Akbarzadeh Khorshidi, H. (2019). A hybrid missing data imputation method for constructing city mobility indices. In R. Islam, Y. S. Koh, Y. Zhao, G. Warwick, D. Stirling, C.-T. Li, & Z. Islam (Eds.), Data Mining - 16th Australasian Conference, AusDM 2018 Bahrurst, NSW, Australia, November 28–30, 2018 Revised Selected Papers (pp. 135-148). (Communications in Computer and Information Science; Vol. 996). Springer. https://doi.org/10.1007/978-981-13-6661-1_11

Nikfalazar, Sanaz ; Yeh, Chung-Hsing ; Bedingfield, Susan Elizabeth et al. / A hybrid missing data imputation method for constructing city mobility indices. Data Mining - 16th Australasian Conference, AusDM 2018 Bahrurst, NSW, Australia, November 28–30, 2018 Revised Selected Papers. editor / Rafiqul Islam ; Yun Sing Koh ; Yanchang Zhao ; Graco Warwick ; David Stirling ; Chang-Tsun Li ; Zahidul Islam. Singapore Singapore : Springer, 2019. pp. 135-148 (Communications in Computer and Information Science).

@inproceedings{34e46495de1e45de9751bbcaf1374443,

title = "A hybrid missing data imputation method for constructing city mobility indices",

abstract = "An effective missing data imputation method is essential for data mining and knowledge discovery from a comprehensive database with missing values. This paper proposes a new hybrid imputation method to effectively deal with the missing data issue of the Mobility in Cities Database (MCD) to construct city mobility indices. The hybrid method integrates the advantages of decision trees and fuzzy clustering into an iterative algorithm for missing data imputation. Extensive experiments conducted on the MCD and three commonly used datasets demonstrate that the hybrid method outperforms other existing effective imputation methods. With the MCD{\textquoteright}s missing values imputed by the hybrid method, and using factor analysis and principal component analysis, this paper constructs city mobility indices for 63 cities in the MCD based on the novel concept of city mobility supply and demand. The city mobility indices constructed under a hierarchical structure of mobility supply and demand indicators represent substantial city mobility knowledge discovered from mining the MCD. The proposed hybrid method represents a significant contribution to missing data imputation research.",

keywords = "City mobility index, Decision tree, Factor analysis, Iterative fuzzy clustering, Missing data imputation, Principal component analysis",

author = "Sanaz Nikfalazar and Chung-Hsing Yeh and Bedingfield, {Susan Elizabeth} and {Akbarzadeh Khorshidi}, Hadi",

year = "2019",

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booktitle = "Data Mining - 16th Australasian Conference, AusDM 2018 Bahrurst, NSW, Australia, November 28–30, 2018 Revised Selected Papers",

note = "Australasian Data Mining Conference 2018, AusDM 2018 ; Conference date: 28-11-2018 Through 30-11-2018",

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Nikfalazar, S , Yeh, C-H, Bedingfield, SE & Akbarzadeh Khorshidi, H 2019, A hybrid missing data imputation method for constructing city mobility indices. in R Islam, YS Koh, Y Zhao, G Warwick, D Stirling, C-T Li & Z Islam (eds), Data Mining - 16th Australasian Conference, AusDM 2018 Bahrurst, NSW, Australia, November 28–30, 2018 Revised Selected Papers. Communications in Computer and Information Science, vol. 996, Springer, Singapore Singapore, pp. 135-148, Australasian Data Mining Conference 2018, Bathurst, New South Wales, Australia, 28/11/18. https://doi.org/10.1007/978-981-13-6661-1_11

A hybrid missing data imputation method for constructing city mobility indices. / Nikfalazar, Sanaz ; Yeh, Chung-Hsing; Bedingfield, Susan Elizabeth et al.
Data Mining - 16th Australasian Conference, AusDM 2018 Bahrurst, NSW, Australia, November 28–30, 2018 Revised Selected Papers. ed. / Rafiqul Islam; Yun Sing Koh; Yanchang Zhao; Graco Warwick; David Stirling; Chang-Tsun Li; Zahidul Islam. Singapore Singapore: Springer, 2019. p. 135-148 (Communications in Computer and Information Science; Vol. 996).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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AU - Yeh, Chung-Hsing

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N2 - An effective missing data imputation method is essential for data mining and knowledge discovery from a comprehensive database with missing values. This paper proposes a new hybrid imputation method to effectively deal with the missing data issue of the Mobility in Cities Database (MCD) to construct city mobility indices. The hybrid method integrates the advantages of decision trees and fuzzy clustering into an iterative algorithm for missing data imputation. Extensive experiments conducted on the MCD and three commonly used datasets demonstrate that the hybrid method outperforms other existing effective imputation methods. With the MCD’s missing values imputed by the hybrid method, and using factor analysis and principal component analysis, this paper constructs city mobility indices for 63 cities in the MCD based on the novel concept of city mobility supply and demand. The city mobility indices constructed under a hierarchical structure of mobility supply and demand indicators represent substantial city mobility knowledge discovered from mining the MCD. The proposed hybrid method represents a significant contribution to missing data imputation research.

AB - An effective missing data imputation method is essential for data mining and knowledge discovery from a comprehensive database with missing values. This paper proposes a new hybrid imputation method to effectively deal with the missing data issue of the Mobility in Cities Database (MCD) to construct city mobility indices. The hybrid method integrates the advantages of decision trees and fuzzy clustering into an iterative algorithm for missing data imputation. Extensive experiments conducted on the MCD and three commonly used datasets demonstrate that the hybrid method outperforms other existing effective imputation methods. With the MCD’s missing values imputed by the hybrid method, and using factor analysis and principal component analysis, this paper constructs city mobility indices for 63 cities in the MCD based on the novel concept of city mobility supply and demand. The city mobility indices constructed under a hierarchical structure of mobility supply and demand indicators represent substantial city mobility knowledge discovered from mining the MCD. The proposed hybrid method represents a significant contribution to missing data imputation research.

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Nikfalazar S , Yeh CH, Bedingfield SE, Akbarzadeh Khorshidi H. A hybrid missing data imputation method for constructing city mobility indices. In Islam R, Koh YS, Zhao Y, Warwick G, Stirling D, Li CT, Islam Z, editors, Data Mining - 16th Australasian Conference, AusDM 2018 Bahrurst, NSW, Australia, November 28–30, 2018 Revised Selected Papers. Singapore Singapore: Springer. 2019. p. 135-148. (Communications in Computer and Information Science). doi: 10.1007/978-981-13-6661-1_11

A hybrid missing data imputation method for constructing city mobility indices

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Keywords

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