| 基于P2P的自适应分布式k最近邻搜索算法 |
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| 引用本文： | 余小高,余小鹏. 基于P2P的自适应分布式k最近邻搜索算法[J]. 计算机工程, 2009, 35(19): 49-52,5 |
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| 作者姓名： | 余小高 余小鹏 |
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| 作者单位： | 湖北经济学院信息管理学院,武汉,430205;武汉工程大学经济管理学院,武汉,430073 |
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| 基金项目： | 湖北省教育厅基金资助项目"群体决策的知识发现与推理模型和方法研究" |
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| 摘 要： | k最近邻搜索算法无法满足数据挖掘的分布性、实时性和可扩展性要求，针对该问题提出基于P2P的自适应分布式k最近邻搜索算法[0]（P2PAKNNs）。闸述GHT＊结构，定义高维数据相似度函数HDSF（X,Y），论述GHT。中的插入算法、范围查找算法和搜索算法。给出P2PAKNNs的实现过程，通过实验证明典正确性。
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| 关 键 词： | k最近邻搜索算法 度量空间 相似性查询 |
| 修稿时间： | |
P2P-based Self-adaptive Distributed k-nearest Neighbor Search Algorithm |
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| YU Xiao-gao,YU Xiao-peng. P2P-based Self-adaptive Distributed k-nearest Neighbor Search Algorithm[J]. Computer Engineering, 2009, 35(19): 49-52,5 |
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| Authors: | YU Xiao-gao YU Xiao-peng |
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| Affiliation: | (1. School of Information Management, Hubei University of Economics, Wuhan 430205; 2. School of Economic Management, Wuhan Institute of Technology, Wuhan 430073) |
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| Abstract: | k-nearest Neighbor search algorithm(KNNs) can not satisfy the needs of distributing, real time performance and expansibility for data mining. Aiming at this problem, a P2P-based self-adaptive distributed KNNs(P2PAKNNs) is proposed. This paper expounds GHT* structure, and gives similarity measure function HDSF(X, Y). Insert algorithm, range find algorithm and search algorithm in GHT* are discussed. Implementation process of P2PAKNNs is given, and its correctness is validated by experiment. |
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| Keywords: | k-nearest Neighbor search algorithm(KNNs) metric space similarity query |
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