Projective ART with buffers for the high dimensional space clustering and an application to discover stock associations

Abstract

Unlike to traditional hierarchical and partitional clustering algorithms which always fail to deal with very large databases, a neural network architecture, projective adaptive resonance theory (PART), is developed for the high dimensional space clustering. However, the success of the PART algorithm depends on both accurate parameters and satisfied orders of input data sets. These disadvantages prevent PART from being applied to realtime databases. In this paper, we propose an improved method, PART with buffer management, to overcome these disadvantages. The major contributions of our method are introducing a buffer management and a new similar degree function and buffer checkout process. The buffer management mechanism allows data sets not to be immediately clustered to one cluster. The purpose of the average similar degree is to successfully work with high similar noise data sets and partly achieve an order-independent objective without correct parameters. And the average similar degree has a good attribute, the parameter-tolerance. Namely, the clustering result does not depend on the precise choice of input parameters, and different parameter values have close clustering results including dimensions associated with clusters. The buffer checkout process can handle a huge amount of input data sets by a small buffer space. Also, simulations and comparisons in high dimensional spaces are reported, and an application by using our algorithm to find stock concurrence association rules is given finally.