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

Luo En-tao; Wang Guo-jun

doi:10.11999/JEIT150422

Volume 37 Issue 12

Jan. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2015 > 37(12): 2795-2801

Luo En-tao, Wang Guo-jun. A Hierarchical Clustering Method Based on the Threshold of Semantic Feature in Big Data[J]. Journal of Electronics & Information Technology, 2015, 37(12): 2795-2801. doi: 10.11999/JEIT150422

Citation:

Luo En-tao, Wang Guo-jun. A Hierarchical Clustering Method Based on the Threshold of Semantic Feature in Big Data[J]. Journal of Electronics & Information Technology, 2015, 37(12): 2795-2801. doi: 10.11999/JEIT150422

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

doi: 10.11999/JEIT150422

Luo En-tao¹,
Wang Guo-jun²

1.
2.
(School of Information Science and Engineering, Central South University, Changsha 410083, China)

Funds:

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

Received Date: 2015-04-10
Rev Recd Date: 2015-09-01
Publish Date: 2015-12-19

Abstract

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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References(21)

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