View in EDS

Structure identification of missing data: a perspective from granular computing.

Saved in:
Bibliographic Details
Title: Structure identification of missing data: a perspective from granular computing.
Authors: Shen, Yinghua1 (AUTHOR), Zhao, Dan1 (AUTHOR), Hu, Xingchen2 (AUTHOR) xhu4@ualberta.ca, Pedrycz, Witold3 (AUTHOR), Chen, Yuan4 (AUTHOR), Li, Jiliang5 (AUTHOR), Xiao, Zhi1 (AUTHOR)
Source: Soft Computing - A Fusion of Foundations, Methodologies & Applications. Mar2026, Vol. 30 Issue 3, p1913-1931. 19p.
Subjects: Missing data (Statistics), Granular computing, Fuzzy clustering technique, Data structures, Clustering algorithms, Statistics
Abstract: Missing data are frequently encountered in reality, which inevitably poses great challenges to data mining techniques devoted to data structure identification. In view of the fact that missing data generally exhibits high uncertainty, this paper first introduces the concept of information granule, and performs granular imputation on missing data in a more abstract and inclusive way. With the tolerant nature of the information granule to uncertainty, the error of data imputation and the adverse effects on the subsequent research caused by the low-quality data can be largely reduced. Second, the initial data structure (including granular cluster centers and numeric partition matrix) of the data set with missing values is identified by performing fuzzy clustering on the mixed data set (including both numeric values and information granules) formed by imputation. Third, the bounds of granular cluster centers are further optimized by using the principle of justifiable granularity, and a more robust and reliable granular partition matrix is formed subsequently. Finally, by constructing a reconstruction criterion for mixed data, clustering performance and the optimization of some critical parameters (e.g., the cluster number) used in the proposed method could be investigated. This paper conducts comprehensive experimental studies on both synthetic and publicly available data sets to show the feasibility and effectiveness of the proposed data structure exploration method. [ABSTRACT FROM AUTHOR]
Copyright of Soft Computing - A Fusion of Foundations, Methodologies & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Description
Abstract:Missing data are frequently encountered in reality, which inevitably poses great challenges to data mining techniques devoted to data structure identification. In view of the fact that missing data generally exhibits high uncertainty, this paper first introduces the concept of information granule, and performs granular imputation on missing data in a more abstract and inclusive way. With the tolerant nature of the information granule to uncertainty, the error of data imputation and the adverse effects on the subsequent research caused by the low-quality data can be largely reduced. Second, the initial data structure (including granular cluster centers and numeric partition matrix) of the data set with missing values is identified by performing fuzzy clustering on the mixed data set (including both numeric values and information granules) formed by imputation. Third, the bounds of granular cluster centers are further optimized by using the principle of justifiable granularity, and a more robust and reliable granular partition matrix is formed subsequently. Finally, by constructing a reconstruction criterion for mixed data, clustering performance and the optimization of some critical parameters (e.g., the cluster number) used in the proposed method could be investigated. This paper conducts comprehensive experimental studies on both synthetic and publicly available data sets to show the feasibility and effectiveness of the proposed data structure exploration method. [ABSTRACT FROM AUTHOR]
ISSN:14327643
DOI:10.1007/s00500-023-09523-9