A fast CAST-based clustering algorithm for very large database

Abstract

The advances in nanometer technology and integrated circuit technology enable the graphics card to attach individual memory and one or more processing units, named GPU, in which most of the graphing instructions can be processed parallelly. Obviously, the computation resource can be used to improve the execution efficiency of not only graphing applications but other time consuming applications like data mining. CAST (Clustering Affinity Search Technique) is a famous clustering algorithm, which is widely used in clustering the biological data. In this paper, we will propose two algorithms, namely Calculation-On-Demand CAST, abbreviated as COD-CAST and Calculation-On-Demand CAST with GPU, abbreviated as COD-CAST-GPU, respectively. The first proposed COD-CAST algorithm is a refined CAST algorithm that can process large amount of objects more efficiently in terms of execution time. The proposed COD-CAST-GPU algorithm can utilize the GPU and the individual memory of graphics card to accelerate the COD-CAST. The experimental results show that our proposed algorithms deliver excellent performance in terms of execution time and required memory.