Exploiting Data Reliability and Fuzzy Clustering for Journal Ranking

Pan Su, Changjing Shang, Tianhua Chen, Qiang Shen

Research output: Contribution to journalArticlepeer-review

33 Citations (Scopus)


Journal impact indicators are widely accepted as possible measurements of academic journal quality. However, much debate has recently surrounded their use, and alternative journal impact evaluation techniques are desirable. Aggregation of multiple indicators offers a promising method to produce a more robust ranking result, avoiding the possible bias caused by the use of a single impact indicator. In this paper, fuzzy aggregation and fuzzy clustering, especially the ordered weighted averaging (OWA) operators are exploited to aggregate the quality scores of academic journals that are obtained from different impact indicators. Also, a novel method for linguistic term-based fuzzy cluster grouping is proposed to rank academic journals. The paper allows for the construction of distinctive fuzzy clusters of academic journals on the basis of their performance with respect to different journal impact indicators, which may be subsequently combined via the use of the OWA operators. Journals are ranked in relation to their memberships in the resulting combined fuzzy clusters. In particular, the nearest-neighbor guided aggregation operators are adopted to characterize the reliability of the indicators, and the fuzzy clustering mechanism is utilized to enhance the interpretability of the underlying ranking procedure. The ranking results of academic journals from six subjects are systematically compared with the outlet ranking used by the Excellence in Research for Australia, demonstrating the significant potential of the proposed approach.
Original languageEnglish
Pages (from-to)1306-1319
Number of pages14
JournalIEEE Transactions on Fuzzy Systems
Issue number5
Early online date21 Sep 2016
Publication statusPublished - 1 Oct 2017
Externally publishedYes


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