Reclassification formula that provides to surpass K-means method

TL;DR

This paper introduces a reclassification formula for multidimensional data that improves clustering stability and efficiency over K-means by focusing on stable partitions with minimal total squared error.

Contribution

The paper proposes a novel reclassification formula that enables the calculation of optimal, stable data partitions, surpassing K-means in efficiency and stability.

Findings

The formula describes the change in total squared error due to reclassification.

The method produces stable partitions with minimal total squared error.

It is more efficient than traditional K-means clustering.

Abstract

The paper presents a formula for the reclassification of multidimensional data points (columns of real numbers, "objects", "vectors", etc.). This formula describes the change in the total squared error caused by reclassification of data points from one cluster into another and prompts the way to calculate the sequence of optimal partitions, which are characterized by a minimum value of the total squared error E (weighted sum of within-class variance, within-cluster sum of squares WCSS etc.), i.e. the sum of squared distances from each data point to its cluster center. At that source data points are treated with repetitions allowed, and resulting clusters from different partitions, in general case, overlap each other. The final partitions are characterized by "equilibrium" stability with respect to the reclassification of the data points, where the term "stability" means that any prescribed reclassification of data points does not increase the total squared error E. It is important that conventional K-means method, in general case, provides generation of instable partitions with overstated values of the total squared error E. The proposed method, based on the formula of reclassification, is more efficient than K-means method owing to converting of any partition into stable one, as well as involving into the process of reclassification of certain sets of data points, in contrast to the classification of individual data points according to K-means method.