CAJ | 학술논문

分层聚类技术在图像处理、入侵检测和生物信息学等方面有着极为重要的应用,是数据挖掘领域的研究热点之一.针对目前基于SIMD模型的并行分层聚类算法处理海量数据时效果不理想的问题,提出一种基于数据预处理的自适应并行分层聚类算法,在O((λn)~2/p)的时间内对n个输入数据点进行聚类.其中1≤p≤n/log n,0.1≤λ≤0.3.将提出的算法与现有文献结论进行的性能对比分析表明,本算法明显改进了现有文献的研究结果.
분층취류기술재도상처리、입침검측화생물신식학등방면유착겁위중요적응용,시수거알굴영역적연구열점지일.침대목전기우SIMD모형적병행분층취류산법처리해량수거시효과불이상적문제,제출일충기우수거예처리적자괄응병행분층취류산법,재O((λn)~2/p)적시간내대n개수입수거점진행취류.기중1≤p≤n/log n,0.1≤λ≤0.3.장제출적산법여현유문헌결론진행적성능대비분석표명,본산법명현개진료현유문헌적연구결과.
Hierarchial clustering technology plays a very important role in image processing, intrusion detection and bioinformatics applications, which is one of the most extensively studied branch in data mining. Presently the parallel hierarchical algorithms aren't very good at processing large data. To overcome this shortcoming, this paper proposed a new parallel algorithm based on preprocessed data. The proposed algorithms could cluster n objects with O(p) processors in O((λn)~2/p) time, where 1≤p≤n/log n,0.1≤λ≤0.3. Performance comparisons show that it is the first parallel hierarchical clustering algorithm without memory conflicts, and thus it is an improved result over the past researches.