K-Means Clustering with KNN and Mean Imputation on CPU Benchmark Compilation Data

Abstract

In the rapidly evolving digital age, data is becoming a valuable source for decision-making and analysis. Clustering, as an important technique in data analysis, has a key role in organizing and understanding complex datasets. One of the effective clustering algorithms is k-means. However, this algorithm is prone to the problem of missing values, which can significantly affect the quality of the resulting clusters. To overcome this challenge, imputation methods are used, including mean imputation and K-Nearest Neighbor (KNN) imputation. This study aims to analyze the impact of imputation methods on CPU Benchmark Compilation clustering results. Evaluation of the clustering results using the silhouette coefficient showed that clustering with mean imputation achieved a score of 0.782, while with KNN imputation it achieved a score of 0.777. In addition, the cluster interpretation results show that the KNN method produces more information that is easier for users to understand. This research provides valuable insights into the effectiveness of imputation methods in improving the quality of data clustering results in assisting CPU selection decisions on CPU Benchmark Compilation data.

Keywords

• clustering
• imputasi knn
• imputasi mean
• k-means
• silhouette coefficient