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Relocating local outliers produced by K-means and K-medoids using local outlier rectifier V. 2.0

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Date
2020-02-06
Author
Badiang, Rogelio O.
Gerardo, Bobby ORCID
Medina, Ruji ORCID
Agrovoc term
data mining AGROVOC
algorithms AGROVOC
information services AGROVOC
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Abstract
The extensive growth in the field of information and communication technology allows easy capture of massive amounts of valuable data in different areas. These data are used in various data mining techniques. However, in some cases, the presence of outliers in the dataset exists. One of the categories of an outlier is the local outlier. Local outliers are data points that deviate locally from the cluster center. They occur when the cluster center, known as centroid or medoid, cannot represent all the data members in the cluster. The unrepresented data are mistakenly classified to their closest clusters, making them local outliers. With this, the study aims to address the problem of local outliers produced by K-means and K-medoids. The Local Outlier Rectifier V.2.0 (LOR V.2.0) is a method used to relocate local outliers to their correct clusters. The simulations show that when LOR V.2.0 is partnered with K-means, it was able to relocate 35.37%, 34.78%, 25%, and 12.28% local outliers of Ionosphere, Breast Cancer Wisconsin, Iris, and Breast Cancer Coimbra datasets, respectively. On the contrary, when LOR V.2.0 is partnered with K-medoids, 29.67% of Breast Cancer Wisconsin, 29.11% of Ionosphere, 25.0% of Iris, and 10.34% of Breast Cancer Coimbra local outliers were transferred to their correct clusters. The result also indicates that the method works better when partnered with K-means.
Contributes to SDGs
SDG 9 - Industry, innovation and infrastructure
URI
https://hdl.handle.net/20.500.14353/610
Recommended Citation
Badiang, R. O., Gerardo, B. D., & Medina, R. P. (2019, October). Relocating local outliers produced by K-means and K-medoids using local outlier rectifier V. 2.0. In 2019 International Conference on Advanced Computer Science and information Systems (ICACSIS) (pp. 89-94). Bali, Indonesia. IEEE. https://doi.org/ 10.1109/ICACSIS47736.2019.8979741
DOI
10.1109/ICACSIS47736.2019.8979741
Type
Conference paper
ISBN
978-1-7281-5292-9
Keywords
K-means K-medoids Local outlier Mahalanobis distance Median absolute deviation
Subject
Data mining OCLC - FAST (Faceted Application of Subject Terminology) Information storage and retrieval systems OCLC - FAST (Faceted Application of Subject Terminology) Information services--User education OCLC - FAST (Faceted Application of Subject Terminology) Computer algorithms OCLC - FAST (Faceted Application of Subject Terminology) Ionosphere OCLC - FAST (Faceted Application of Subject Terminology)
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