Imputing missing values for mixed numeric and categorical attributes based on incomplete data hierarchical clustering

12Citations
Citations of this article
6Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Missing data imputation is a key issue of data pre-processing in data mining field. Though there are many methods for missing value imputation, almost each of these imputation methods has its limitation and is designed for either numeric attributes or categorical attributes. This paper presents IMIC, a new missing value Imputation method for Mixed numeric and categorical attributes based on Incomplete data hierarchical clustering after the introduction of a new concept Incomplete Set Mixed Feature Vector (ISMFV). The effect of the new method is valuated through the comparison experiment using 3 real data sets from UCI. © 2011 Springer-Verlag.

Cite

CITATION STYLE

APA

Feng, X., Wu, S., & Liu, Y. (2011). Imputing missing values for mixed numeric and categorical attributes based on incomplete data hierarchical clustering. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7091 LNAI, pp. 414–424). https://doi.org/10.1007/978-3-642-25975-3_37

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free