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Density weighted averaging operator and application

  • Complex Science Management
  • Published:
Wuhan University Journal of Natural Sciences

Abstract

Exploring structural characteristics implied in initialdecision making information is an important issue in the process of aggregation. In this paper we provide a new family of aggregation operator called density weighted averaging operator (abbreviated as DWA operator), which carries out the aggregation by classification. In this case, not only the hidden structural characteristics can be identified, some commonly known aggregation operators can also be incorporated into the function of the DWA operator. We further discuss the basic properties of this new operator, such as commutativity, idempotency, boundedness and monotonicity withcertain condition. Afterwards, two important issues related to the DWA operator are investigated, including the arguments partition and the determination of density weights. At last a numerical example regarding performance evaluation of employees is developed to illustrate the using of this new operator.

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References

  1. Yager R R. On ordered weighted averaging aggregation operators in multicriteria decision making [J]. IEEE Transactions on Systems, Man, and Cybernetics, 1998, 18(1): 183–190.

    Article  Google Scholar 

  2. Yage R R. Induced ordered weighted averaging operators[J]. IEEE Transactions on Systems, Man, and Cybernetics-Part B, 1999, 29(2): 141–150.

    Article  Google Scholar 

  3. Merigo J, Gillafuente A. The induced generalized OWA operator [J]. Information Sciences, 2009, 179(6): 729–741.

    Article  Google Scholar 

  4. Zhou L G, Chen H Y. Continuous generalized OWA operator and its application to decision making [J]. Fuzzy Sets and Systems, 2011, 168(1): 18–34.

    Article  Google Scholar 

  5. Merigó J M, Gil-Lafuente A M, Martorell O. Uncertain induced aggregation operators and its application in tourism management [J]. Expert Systems with Application, 2012, 39(1): 869–880.

    Article  Google Scholar 

  6. Xu Z S. Dependent uncertain ordered weighted aggregation operators [J]. Information Fusion, 2008, 9(2): 310–316.

    Article  Google Scholar 

  7. Xu Z S, Da Q L. The uncertain OWA operator [J]. International Journal of Intelligent Systems, 2002, 17(6): 569–575.

    Article  Google Scholar 

  8. Zhou L G, Chen H Y. A generalization of the power aggregation operators for linguistic environment and its application in group decision making [J]. Knowledge-Based Systems, 2012, 26: 216–224.

    Article  Google Scholar 

  9. Xu Z S. A method based on linguistic aggregation operators for group decision making with linguistic preference relations[ J]. Information Sciences, 2004, 166(1): 19–30.

    Article  Google Scholar 

  10. Casanovas M, Merigó J M. Fuzzy aggregation operators in decision making with Dempster-Shafer belief structure[J]. Expert Systems with Applications, 2012, 39(8): 7138–7149.

    Article  Google Scholar 

  11. Wei G. Some induced geometric aggregation operators with intuitionistic fuzzy information and their application to group decision making [J]. Applied Soft Computing, 2010, 10(2): 423–431.

    Article  Google Scholar 

  12. Xu Z S. Uncertain linguistic aggregation operators based approach to multiple attribute group decision making under uncertain linguistic environment [J]. Information Sciences, 2004, 168(1-4): 171–184.

    Article  Google Scholar 

  13. Goldberger J, Tassa T. A hierarchical clustering algorithm based on the Hungarian method [J]. Pattern Recognition Letters, 2008, 29(11): 1632–1638.

    Article  Google Scholar 

  14. Murat E, Nazif C, Sadullah S. A new algorithm for initial cluster centers in k-means algorithm [J]. Pattern Recognition Letters, 2011, 32(14): 1701–1705.

    Article  Google Scholar 

  15. Chiang M C, Tsai C W, Yang C S. A time-efficient pattern reduction algorithm for k-means clustering[J]. Information Sciences, 2011, 181(4): 716–731.

    Article  Google Scholar 

  16. Hathaway R J, Bezdek J C, Davenport J W. On relational data versions of c-means algorithms [J]. Pattern Recognition Letters, 1996, 17(6): 607–610.

    Article  Google Scholar 

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Correspondence to Danning Zhang.

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Foundation item: Supported by the National Natural Science Foundation of China (71671031, 71701040)

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Zhang, D., Yi, P. & Li, W. Density weighted averaging operator and application. Wuhan Univ. J. Nat. Sci. 22, 535–540 (2017). https://doi.org/10.1007/s11859-017-1285-7

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  • DOI: https://doi.org/10.1007/s11859-017-1285-7

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