The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Standardization and Its Effects on K-Means Clustering Algorithm
Open accessAbstract
Data clustering is an important data exploration technique with many applications in data mining. K-means is one of the most well known methods of data mining that partitions a dataset into groups of patterns, many methods have been proposed to improve the performance of the K-means algorithm. Standardization is the central preprocessing step in data mining, to standardize values of features or attributes from different dynamic range into a specific range. In this paper, we have analyzed the performances of the three standardization methods on conventional K-means algorithm. By comparing the results on infectious diseases datasets, it was found that the result obtained by the z-score standardization method is more effective and efficient than min-max and decimal scaling standardization methods.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Jain, A. K., Murty, M. N., & Flynn, P. J. (1999) · ACM Computing Surveys · doi:10.1145/331499.331504 · Cited by 13,199
Algorithms for Clustering Data
Sarle, W. S. (1990) · Technometrics · doi:10.1080/00401706.1990.10484648 · Cited by 7,865
Algorithms for clustering data
Flury, B. (1989) · Journal of Statistical Planning and Inference · doi:10.1016/0378-3758(89)90033-5 · Cited by 6,432
Discovering knowledge in data: an introduction to data mining
(2005) · Choice Reviews Online · doi:10.5860/choice.42-4687 · Cited by 1,347
A study of standardization of variables in cluster analysis
Milligan, G. W., & Cooper, M. C. (1988) · Journal of Classification · doi:10.1007/bf01897163 · Cited by 877
(2007) · doi:10.1007/978-0-387-36795-8 · Cited by 613
Data Mining: A Preprocessing Engine
Shalabi, L. A., Shaaban, Z., & Kasasbeh, B. (2006) · Journal of Computer Science · doi:10.3844/jcssp.2006.735.739 · Cited by 612
Feature normalization and likelihood-based similarity measures for image retrieval
Aksoy, S., & Haralick, R. M. (2001) · Pattern Recognition Letters · doi:10.1016/s0167-8655(00)00112-4 · Cited by 457
Impact of Outlier Removal and Normalization Approach in Modified k-Means Clustering Algorithm
Patel, V., Mehta, R. G., & Vallabhbhai, S. (2011) · Cited by 81
Cited by
A Multimodal Deep Learning Method for Android Malware Detection Using Various Features
Kim, T., Kang, B., Rho, M., Sezer, S., & Im, E. G. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2018.2866319 · Cited by 518
Toward a Wearable System for Predicting Freezing of Gait in People Affected by Parkinson's Disease
Demrozi, F., Bacchin, R., Tamburin, S., Cristani, M., & Pravadelli, G. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2952618 · Cited by 86
Data Analytics: Why Data Normalization
K. Dhana Sree, D., & C. Shoba Bindu, D. (2018) · International Journal of Engineering & Technology · doi:10.14419/ijet.v7i4.6.20464 · Cited by 22
Metadata
- Authors
- Mohamad, I. B., & Usman, D.
- Venue
- Research Journal of Applied Sciences, Engineering and Technology
- Publisher
- Maxwell Scientific Publication Corp.
- Published
- 2013-09-20
- Volume
- 6
- Issue
- 17
- Pages
- 3299-3303
- ISSN
- 2040-7459 (ISSN-L)
- 2040-7467
- OpenAlex
- W2184638288
- Cited by
- 411
- References
- 9
- FWCI
- 7.36
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Standardization and Its Effects on K-Means Clustering Algorithm. https://app.cochat.ai/papers/10.19026/rjaset.6.3638
- The paper
- Mohamad, I. B., & Usman, D. (2013). Standardization and Its Effects on K-Means Clustering Algorithm. Research Journal of Applied Sciences, Engineering and Technology. https://doi.org/10.19026/rjaset.6.3638
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
