The Application of GPU-based K-Means in Analysis of RFID Data

Huifang Deng, Zhen Liang, and Chunhui Deng


RFID, Data Mining, cluster analysis, Fuzzy Adaptive Resonance Theory


With widespread applications of RFID (Radio Frequency Identification) technology, RFID data are growing at an exponential rate. More and more companies use Data Mining to find information which is the most relevant to the company business from mass of data. K-Means algorithm is one of the main algorithms of Data Mining. CUDA (Compute Unified Device Architecture) introduced by NVIDIA for GPGPU (General-Purpose computation on Graphics Processing Units) programming is a very powerful tool to accelerate K-Means. In this paper, we proposed a new set of parallel solution of K-Means algorithm called BG K-Means (Batch GPU-based K-Means) to implement K-Means based on GPU with CUDA. Compared with existing GPU-based K-Means algorithm, by taking so-called “batch” approach, BG K-Means makes full and rational use of CUDA's memories (shared memory, global memory, and constant memory) and reduces the access to data set. Hence, the speed of BG K-Means could reach as high as 55 times that of the CPU-based K-Means. Finally, we designed a system based on RFID data, and applied BG K-Means to data analysis.

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