On Nonstationary Gaussian Process Model for Solving Data-Driven Optimization Problems

Hu, Caie; Zeng, Sanyou; Li, Changhe; Zhao, Fei

doi:10.1109/tcyb.2021.3120188

Cited by 5 publications

(1 citation statement)

References 55 publications

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“…To improve K-Modes, the distance measure is changed rather than using simple matching, and the result is compared to the standard K-Modes algorithm or regular K-Means. [8]…”

Section: Research Objectmentioning

confidence: 99%

Optimization in Time and Score using IID Algorithm for K-Modes Clustering

Yulianti

Sen

2023

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Nowadays, there are numerous methods for analyzing data, one of which is cluster analysis. Because most practical data in today's analysis contains categorical attributes, categorical data clustering has recently received a lot of attention. To cluster categorical data, unsupervised machine learning techniques, which used frequency-based method, such as K-Mode’s clustering are used. The K-Modes algorithm takes advantage of the differences between the data points (total mis-matches or dissimilarities). The lower the dissimilarities, the more similar the data points, and thus the better the cluster. This paper aims to improve K-Mode’s clustering performance by incorporating the intercluster and intracluster dissimilari-ty measure, or IID measure, into the K-Modes algorithm rather than just using the standard simple-matching method to increase the algorithm's accuracy and execution time. This combined algorithm improves accuracy and execution time of the K-Modes algorithm. As a result, this algorithm can be used as an alternative to better cluster categorical data.

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“…To improve K-Modes, the distance measure is changed rather than using simple matching, and the result is compared to the standard K-Modes algorithm or regular K-Means. [8]…”

Section: Research Objectmentioning

confidence: 99%