K-Splits: Improved K-Means Clustering Algorithm to Automatically Detect the Number of Clusters
This addresses the need for efficient and accurate clustering in data analysis, though it is incremental as it builds on existing k-means methods.
The paper tackles the problem of automatically detecting the number of clusters in data without prior knowledge, introducing k-splits, an improved hierarchical algorithm based on k-means that achieves excellent accuracy in finding the correct number of clusters and is faster than similar methods, even faster than standard k-means in lower dimensions.
This paper introduces k-splits, an improved hierarchical algorithm based on k-means to cluster data without prior knowledge of the number of clusters. K-splits starts from a small number of clusters and uses the most significant data distribution axis to split these clusters incrementally into better fits if needed. Accuracy and speed are two main advantages of the proposed method. We experiment on six synthetic benchmark datasets plus two real-world datasets MNIST and Fashion-MNIST, to prove that our algorithm has excellent accuracy in finding the correct number of clusters under different conditions. We also show that k-splits is faster than similar methods and can even be faster than the standard k-means in lower dimensions. Finally, we suggest using k-splits to uncover the exact position of centroids and then input them as initial points to the k-means algorithm to fine-tune the results.