K-Means Clustering and Multilayer Perceptron for  Categorizing Student Business Groups

Walid, Miftahul; Sahbaniya, Norfiah Lailatin Nispi; Hozairi, Hozairi; Baskoro, Fajar; Wijaya, Arya Yudhi

doi:10.17977/um018v6i12023p69-78

Cited by 1 publication

(1 citation statement)

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“…The preprocessing step using K-Means clustering helps in reducing data complexity by grouping similar data points into clusters (Usman & Stores, 2020). This process enables the MLP model to focus on relevant features and avoid memorizing noise or irrelevant patterns present in the data, thus mitigating the risk of overfitting (Walid et al, 2023).…”

Section: Preprocessing Using K-meansmentioning

confidence: 99%

Enhancing Multi-Layer Perceptron Performance with K-Means Clustering

Pardede,

Ichsan,

Riyadi

2024

CNAHPC

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Machine learning plays a crucial role in identifying patterns within data, with classification being a prominent application. This study investigates the use of Multilayer Perceptron (MLP) classification models and explores preprocessing techniques, particularly K-Means clustering, to enhance model performance. Overfitting, a common challenge in MLP models, is addressed through the application of K-Means clustering to streamline data preparation and improve classification accuracy. The study begins with an overview of overfitting in MLP models, highlighting the significance of mitigating this issue. Various techniques for addressing overfitting are reviewed, including regularization, dropout, early stopping, data augmentation, and ensemble methods. Additionally, the complementary role of K-Means clustering in enhancing model performance is emphasized. Preprocessing using K-Means clustering aims to reduce data complexity and prevent overfitting in MLP models. Three datasets - Iris, Wine, and Breast Cancer Wisconsin - are employed to evaluate the performance of K-Means as a preprocessing technique. Results from cross-validation demonstrate significant improvements in accuracy, precision, recall, and F1 scores when employing K-Means clustering compared to models without preprocessing. The findings highlight the efficacy of K-Means clustering in enhancing the discriminative power of MLP classification models by organizing data into clusters based on similarity. These results have practical implications, underlining the importance of appropriate preprocessing techniques in improving classification performance. Future research could explore additional preprocessing methods and their impact on classification accuracy across diverse datasets, advancing the field of machine learning and its applications

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Section: Preprocessing Using K-meansmentioning

confidence: 99%