大数据中一种基于语义特征阈值的层次聚类方法

罗恩韬; 王国军

doi:10.11999/JEIT150422

大数据中一种基于语义特征阈值的层次聚类方法

doi: 10.11999/JEIT150422

罗恩韬¹,
王国军²

2.
(中南大学信息科学与工程学院长沙 410083) ②(湖南科技学院电子与信息学院永州 425006)

基金项目:

国家自然科学基金(60173037, 6272496, 61272151)，湖南省教育厅科研项目(2015C0589)，湖南科技学院重点学科项目

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- 被引次数: 0
出版历程
- 收稿日期: 2015-04-10
- 修回日期: 2015-09-01
- 刊出日期: 2015-12-19

A Hierarchical Clustering Method Based on the Threshold of Semantic Feature in Big Data

Luo En-tao¹,
Wang Guo-jun²

2.
(School of Information Science and Engineering, Central South University, Changsha 410083, China)

Funds:

The National Natural Science Foundation of China (60173037, 6272496, 61272151)

摘要

摘要: 云计算、健康医疗、街景地图服务、推荐系统等新兴服务促使数据的种类和规模以前所未有的速度增长，数据量的激增会导致很多共性问题。例如数据的可表示，可处理和可靠性问题。如何有效处理和分析数据之间的关系，提高数据的划分效率，建立数据的聚类分析模型，已经成为学术界和企业界共同亟待解决的问题。该文提出一种基于语义特征的层次聚类方法，首先根据数据的语义特征进行训练，然后在每个子集上利用训练结果进行层次聚类，最终产生整体数据的密度中心点，提高了数据聚类效率和准确性。此方法采样复杂度低，数据分析准确，易于实现，具有良好的判定性。
- 大数据 /
- 数据抽取 /
- 层次聚类 /
- 聚类分析
Abstract: The type and scale of data has been promoted with a hitherto unknown speed by the emerging services including cloud computing, health care, street view services recommendation system and so on. However, the surge in the volume of data may lead to many common problems, such as the representability, reliability and handlability of data. Therefore, how to effectively handle the relationship between the data and the analysis to improve the efficiency of classification of the data and establish the data clustering analysis model has become an academic and business problem, which needs to be solved urgently. A hierarchical clustering method based on semantic feature is proposed. Firstly, the data should be trained according to the semantic features of data, and then is used the training result to process hierarchical clustering in each subset; finally, the density center point is produced. This method can improve the efficiency and accuracy of data clustering. This algorithm is of low complexity about sampling, high accuracy of data analysis and good judgment. Furthermore, the algorithm is easy to realize.
- Big data /
- Data extraction /
- Hierarchical clustering /
- Clustering analysis

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