| 基于数据流的概念聚类 |
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| 引用本文： | 史金成,胡学钢.基于数据流的概念聚类[J].计算机工程,2010,36(9):62-64. |
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| 作者姓名： | 史金成 胡学钢 |
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| 作者单位： | 1. 铜陵学院数学与计算机科学系,铜陵,244000
2. 合肥工业大学计算机与信息学院,合肥,230009 |
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| 基金项目： | 安徽省高校省级自然科学基金资助项目(KJ2010B455);;安徽省高等学校优秀青年人才基金资助项目(2008jq1143);;铜陵学院院级科研基金资助项目(2009tlxy21) |
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| 摘 要： | 分析二部图的二元组和概念聚类问题之间的关系，在此基础上结合数据流的特点，提出一种适用于对象属性为布尔型的数据流概念聚类算法。将数据流分段，对每一批到来的数据流，生成局部的近似极大ε二元组集合，对全局的近似极大ε二元组集合进行更新，从而有效地对整个数据流进行聚类。实验结果表明，该算法具有良好的时间效率和空间效率。
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| 关 键 词： | 数据流 概念聚类 近似极大ε二元组 |
| 修稿时间： | |
Conceptual Clustering Based on Data Streams |
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| SHI Jin-cheng,HU Xue-gang.Conceptual Clustering Based on Data Streams[J].Computer Engineering,2010,36(9):62-64. |
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| Authors: | SHI Jin-cheng HU Xue-gang |
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| Affiliation: | (1. Department of Mathematics and Computer Science, Tongling College, Tongling 244000; 2. School of Computer & Information, Hefei University of Technology, Hefei 230009) |
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| Abstract: | Connections between bipartite graph’s bicliques and conceptual clustering are analysed.On the basis,according to the features of data streams,a new conceptual clustering algorithm is proposed to cluster data streams whose attributes are boolean.The data streams are partitioned into a set of segments,and with the arrival of each segment,a local set of approximate maximum ε-bicliques is generated,then the algorithm updates the global set effectively to cluster the entire data streams.Experimental results show that the algorithm has good time efficiency and space efficiency. |
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| Keywords: | data streams conceptual clustering approximate maximum ε-bicliques |
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