Bubble agglomeration algorithm for unsupervised classification: a new clustering methodology without a priori information

The present paper introduces a new unsupervised clustering algorithm, named Bubble Agglomeration (BA). The algorithm deals with each data point as a centre of a bubble with a radius r. All the bubble have the same size, each set of contiguous bubbles forms a natural cluster or a core. The algorithm gradually increases the bubble radius and the number of adjacent bubbles. The number of cores of the expected clusters is consequently decreases. The sparse data points are distributed into the cores obtained according to their distances from different cores. The optimum bubble radius is determined via the reliability curve. Two simulated data sets and three real data sets are employed to validate the performance of the method. A comparison with the K-means cluster analysis shows satisfactory performance of the BA approach.