Comparison of Manhattan and Chebyshev Distance Metrics in Quantum-Based K-Medoids Clustering

Solikhun Solikhun, Muhammad Rahmansyah Siregar, Lise Pujiastuti, Mochamad Wahyudi, Deny Kurniawan

Abstract


Anemia is a condition characterized by a decrease in the number of red blood cells or hemoglobin levels in the bloodstream. It can lead to fatigue and reduced productivity. Clustering is a technique in data mining used to identify patterns that can support decision-making processes. In the case of anemia, clustering plays a crucial role in identifying various severity patterns and understanding the contributing factors behind the condition. Quantum computers, which utilize the principles of quantum mechanics for information processing, have made significant advancements over the past decade. Quantum computing is an advanced method of information processing that leverages qubits, enabling systems to exist in multiple states simultaneously. This technology offers the potential to solve complex problems at exponentially faster speeds than classical computers. In this study, researchers applied the K-Medoids clustering algorithm, calculated using quantum-based equations. The research compares two distance measurement methods: Chebyshev distance and Manhattan distance. The results show that the Manhattan algorithm performs better in medical contexts, particularly for detecting positive cases, with a recall of 0.57 and an F1-score of 0.695, although it has a slightly lower precision of 0.88. This makes it more suitable for medical applications where false negatives carry high risks, such as disease detection, despite its higher cost and mean squared error (MSE). On the other hand, Chebyshev distance achieved perfect precision (1.0) and higher accuracy (80%), but its low recall (0.33) indicates that many positive cases were missed. Therefore, Manhattan distance is more recommended for medical applications that require the detection of more positive cases, while Chebyshev is more efficient for scenarios that prioritize accuracy and cost.

Keywords


Data Mining; Komputasi Kuantum; Clustering; K-Medoid;Jarak Manhattan; Jarak Chebyshev

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References


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DOI: https://doi.org/10.32520/stmsi.v14i4.5193

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