Clustering Accuracy Improvement Using Modified Min-Max Normalization Technique
Maradana Durga Venkata Prasad, T. Srikanth · Preprints.org · 2024
Clustering algorithm such as k-Means is highly sensitive to the scale of input features. A common approach to mitigate this issue is Min-Max scaling normalization, which rescales feature values to a specified range. This paper investigates an alternative form of Min-Max scaling, where the normalization is based on both the minimum (Xmin) and mean (Xmean) of the feature, rather than the maximum value. The proposed method is shown to be particularly effective in improving clustering accuracy for datasets with varying scales and distributions. Experimental results demonstrate that using this modified Min-Max scaling approach leads to better-defined clusters, enhanced performance in terms of clustering accuracy, and reduced bias in distance-based clustering algorithms. We validate the method using several standard clustering techniques, including k-Means on publicly available datasets.