全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

基于密度的线数据分组算法研究

DOI: 10.3724/SP.J.1047.2015.00538, PP. 538-546

Keywords: 分而治之,负载均衡,并行计算,分布不均匀,线数据分组

Full-Text   Cite this paper   Add to My Lib

Abstract:

目前,地理空间数据面临着由于数据量膨胀和计算量高速增长而引起算法效率低的问题,采用"分而治之"的数据分组策略提高运算效率已成为研究的热点。面向分布不均匀的线数据,本文提出了基于密度的线数据分组算法(简称LGAD)。首先,算法通过查找高密度区提取样本线段,保证了分组算法的起点落到高密区;其次,考虑线空间拓扑关系的复杂性,引用水平、垂直和夹角距离度量线段间距离,创建样本线段与其他线段的距离矩阵;最后,以距离矩阵和最优选择方法实现数据负载均衡分组。实验结果显示,对数据分组和分组后数据进行线段聚类的2个过程中,该算法体现了较好的时间优势,与串行计算相比,在分组数为2-12时,平均比率达4.3,提高了应用的响应速度,具有较好的实际意义。

References

[1]  Florida-San Román L. Proposition of two layered ionic structures, with xy disorder but z-ordered, in a quasi-liquid system[J]. Revista mexicana de física, 2006,52:208-210.
[2]  Sarwat M, Mokbel M F, Zhou X, et al. Generic and efficient framework for search trees on flash memory storage systems[J]. GeoInformatica, 2013,17(3):417-448.
[3]  Lee J G, Han J, Whang K Y. Trajectory clustering: A partition-and-group framework[C]. Proceedings of the 2007 ACM SIGMOD international conference on Management of data, 2007:593-694.
[4]  Jonk A, Smeulders AW. An axiomatic approach to clustering line-segments[C]. Proceedings of the Third International Conference on Document Analysis and Recognition, 1995:386-389.
[5]  Kelly A R, Hancock E R. Grouping-line segments using eigenclustering[C]. Proceedings of the British Machine Vision Conference, 2000:1-10.
[6]  Thomas J C R. A new clustering algorithm based on kmeans using a line segment as prototype[C]. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 2011:638-645.
[7]  Gaffney S J, Robertson A W, Smyth P, et al. Probabilistic clustering of extratropical cyclones using regression mixture models[J]. Climate dynamics, 2007,29(4):423-440.
[8]  Vinh N N, Le B. Simple spatial clustering algorithm based on R-tree[J]. Multi-disciplinary Trends in Artificial Intelligence,2012:236-245.
[9]  Wang S, Armstrong M P. A quadtree approach to domain decomposition for spatial interpolation in grid computing environments[J]. Parallel Computing, 2003,29(10):1481-1504.
[10]  赵春宇, 孟令奎, 林志勇. 一种面向并行空间数据库的数 据划分算法研究[J]. 武汉大学学报:信息科学版,2006,31 (11):962-965.
[11]  Guttman A. R-trees: A dynamic index structure for spatial searching[J]. ACM, 1984(14):47-57.
[12]  Jacob C, Maheswaran G, Santosh Kumar I. Clustering in Hilbert R trees: A study on spatial indexing in R trees[J]. Spatial Indexing using R Trees, 2013:1-3.
[13]  Guan Q, Clarke K C. A general-purpose parallel raster processing programming library test application using a geographic cellular automata model[J]. International Jour-nal of Geographical Information Science, 2010,24(5):695-722.
[14]  Farber R. CUDA application design and development[M]. Waltham, MA: Elsevier Inc, 2011:18-19.
[15]  Kim Y, Shim K, Kim M S, et al. DBCURE-MR: An efficient density-based clustering algorithm for large data using MapReduce[J]. Information Systems, 2014,42:15-35.
[16]  Li L, Xi Y. Research on clustering algorithm and its parallelization strategy[C]. 2011 International Conference on Computational and Information Sciences (ICCIS), 2011: 325-328.
[17]  He Y, Tan H, Luo W, et al. An efficient parallel densitybased clustering algorithm using MapReduce[C]. 2011 IEEE 17th International Conference on Parallel and Distributed Systems (ICPADS), 2011:473-480.
[18]  Abugov D. Oracle spatial partitioning: Best practices (an Oracle white paper)[M]. Redwood, CA: Oracle Inc, 2004.
[19]  Meng L, Huang C, Zhao C, et al. An improved Hilbert curve for parallel spatial data partitioning[J]. Geo-spatial Information Science, 2007,10(4):282-286.
[20]  Marshall P L, Region V F. Using line intersect sampling for coarse woody debris[R]. Nanaimo, Canada: Vancouver Forest Region, 2000:112-121.
[21]  Thompson S K. Line-intercept sampling[A]. In: Sampling
[22]  [M]. Hoboken, NJ: John Wiley & Sons Inc, 2012:275-282.
[23]  Fischer F, Kleinn C, Fehrmann L, et al. A national level forest resource assessment for Burkina Faso -a field based forest inventory in a semiarid environment combining small sample size with large observation plots[J]. Forest Ecol. Manage, 2011,262(8):1532-1540.
[24]  Fehrmann L, Seidel D, Krause B, et al. Sampling for landscape elements—A case study from Lower Saxony, Germany[J]. Environmental monitoring and assessment, 2014,186(3):1421-1430.
[25]  全国统计专业技术资格考试用书编写委员会, 统计业务 知识[M]. 北京:中国统计出版社,2011:245-272.
[26]  Whittaker E T. On the functions which are represented by the expansions of the interpolation-theory[R]. Edinburgh, Scotland: Edinburgh University, 1915:181-194.
[27]  柳盛,吉根林,李文俊.一种基于连接度的空间线对象聚 类算法[J].计算机科学,2011,38(8):179-181.
[28]  Merkle R, Hellman M E. Hiding information and signatures in trapdoor knapsacks[C]. IEEE Transactions on Information Theory,1978,24(5):525-530.
[29]  Nievergelt J H H, Sevcik K C. The grid file: An adaptable, symmetric multikey file structure ACM Trans[J]. Database Syst, 1984,9:38-71.
[30]  Dai B R, Lin I C. Efficient map/reduce-based DBSCAN algorithm with optimized data partition[C]. 2012 IEEE 5th International Conference on Cloud Computing (CLOUD), 2012:59-66.
[31]  Xu X, J?ger J, Kriegel H-P. A fast parallel clustering algorithm for large spatial databases[M]. High Performance Data Mining. Springer, 2002:263-290.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133