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| Keyword-Based Clustering Ensembles in Academic Documents |
| Zhang Yingyi1,2, Zhang Chengzhi1,2, Chen Guo1 |
1.Department of Information Management, Nanjing University of Science & Technology, Nanjing 210094
2.Institute of Scientific and Technical Information of China, Beijing 100038 |
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Abstract Clustering is an unsupervised, efficient method of classifying articles. Keyphrases are regarded as the main content of articles, and clustering according to keyphrases is an effective method of performing a text clustering task. However, studies of publication clustering utilize only the clustering algorithms. To date, several methods have had been proposed to improve the performance of clustering, and ensemble clustering is one of them. Thus, based on the concept of ensemble clustering, this paper details a study of keyphrase-based publication clustering. To analyze whether ensemble clustering is an efficient method of publication clustering, this paper compares the performance of ensemble clustering with clustering without ensemble learning. Moreover, to analyze the impact of keyphrases on ensemble clustering, this paper compares the results of various keyphrase extraction methods and various keyphrase quantities based on publication clustering. The experimental results show that ensemble clustering can improve the performance of publication clustering. Publication clustering also yields a better result when the TextRank algorithm is used for extracting keyphrases. The performance also improves when provided with more keyphrases.
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Received: 17 July 2018
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