A study on missing values imputation using K-Harmonic means algorithm: Mixed datasets

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

Data cleaning is one step in the preprocessing which in the process often found missing values in the dataset. Missing values is the condition of the absence of data items on a subject. A quick step that can be taken to handle missing values is to remove data containing missing values, but this can reducing information in the data. Another way to handle missing values is by using imputation with mean, median, or mode, and several methods of imputation such as regression, likelihood, and the clustering approach. Imputation with the clustering approach is the focus of this study, where we used the K-Harmonic Means which has been adjusted to handle mixed data. K-Harmonic Means is an extension of K-Means by reducing random centroid initialization sensitivity problems. Imputation of the missing values is carried out by distributing missing values observation to the cluster and replacing the missing values with the information on the same centroid cluster. The results of the simulation were evaluated using the root mean square error and the accuracy values of each imputation value for numerical and categorical data respectively.

Original languageEnglish
Title of host publicationInternational Conference on Science and Applied Science, ICSAS 2019
EditorsA. Suparmi, Dewanta Arya Nugraha
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735419537
DOIs
Publication statusPublished - 27 Dec 2019
EventInternational Conference on Science and Applied Science 2019, ICSAS 2019 - Surakarta, Indonesia
Duration: 20 Jul 2019 → …

Publication series

NameAIP Conference Proceedings
Volume2202
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

ConferenceInternational Conference on Science and Applied Science 2019, ICSAS 2019
Country/TerritoryIndonesia
CitySurakarta
Period20/07/19 → …

Fingerprint

Dive into the research topics of 'A study on missing values imputation using K-Harmonic means algorithm: Mixed datasets'. Together they form a unique fingerprint.

Cite this