ĐỘ ĐO TƯƠNG TỰ MỚI TRÊN CÁC TẬP MỜ BỨC TRANH VÀ ỨNG DỤNG TRONG PHÂN CỤM DỮ LIỆU

Ngày nhận bài: 08-07-2019

Ngày duyệt đăng: 26-08-2019

DOI:

Lượt xem

0

Download

0

Số: Tập 17 Số 5 (2019)

Chuyên mục:

KỸ THUẬT VÀ CÔNG NGHỆ

Cách trích dẫn:

Thùy, L., Hải, N., Hạnh, N., & Huệ, Đỗ. (2024). ĐỘ ĐO TƯƠNG TỰ MỚI TRÊN CÁC TẬP MỜ BỨC TRANH VÀ ỨNG DỤNG TRONG PHÂN CỤM DỮ LIỆU. Tạp Chí Khoa học Nông nghiệp Việt Nam, 17(5), 386–396. http://testtapchi.vnua.edu.vn/index.php/vjasvn/article/view/562

ĐỘ ĐO TƯƠNG TỰ MỚI TRÊN CÁC TẬP MỜ BỨC TRANH VÀ ỨNG DỤNG TRONG PHÂN CỤM DỮ LIỆU

Lê Thị Diệu Thùy (*) 1 , Nguyễn Hữu Hải 1 , Nguyễn Văn Hạnh 1 , Đỗ Thị Huệ 1

  • 1 Khoa Công nghệ thông tin, Học viện Nông nghiệp Việt Nam

    • Từ khóa

    Tập mờ bức tranh, độ đo tương tự, bài toán phân cụm

    Tóm tắt


    Chỉ số Jaccard là một chỉ số trong thống kê dùng để so sánh độ giống nhau và sự đa dạng giữa các bộ mẫu. Trong bài báo này chúng tôi đề xuất một độ đo tương tự mới giữa các tập mờ bức tranh dựa trên chỉ số Jaccard.Sau đó chúng tôi đưa ra một số ví dụ cho thấy độ đo tương tự mới đã khắc phục được những hạn chế của các độ đo tương tự đã có. Cuối cùng chúng tôi sử dụng độ đo tương tự mới vào bài toán phân cụm dữ liệu.

    Tài liệu tham khảo

    Atanassov K.T. (1986). Intuitionistic fuzzy sets. Fuzzy sets and Systems. 20(1):87-96.

    CuongB.C. (2014). Picture fuzzy sets. Journal of Computer Science and Cybernetics. 30(4):409- 420.

    Cuong B.C.& Hai P.V. (2015). Some fuzzy logic operators for picture fuzzy sets. SeventhInternational Conference on Knowledge and Systems Engineering.pp. 132-137.

    Dengfeng L.& Chuntian C. (2002). New similarity measures of intuitionistic fuzzy sets andapplication to pattern recognitions. Pattern Recogn. Lett. 23: 221-225.

    Dinh N.V. &Thao N.X. (2018). Some Measures of Picture Fuzzy Sets and Their Application in Multi-attribute Decision Making. I.J. Mathematical Sciences and Computing.3:23-41.

    DinhN.V., Thao N.X. &Chau N.M. (2015). On the picture fuzzy database: theories and application. Việt Nam National University of Agriculture, J. Sci. & Devel.13(6):1028-1035.

    Dutta P. (2017). Medical Diagnosis via Distance Measures on Picture Fuzzy Sets. Amse Journals-Amseiieta publication. 54(2): 137-152.

    Garg H. (2017). Some picture fuzzy aggregation operators and their applications to multicriteria decision-making. Arabian Journal for Science Engineering. 42(12):5275-5290.

    Hoa N.D.,Son L.H. &Thong P.H. (2014). Weather nowcasting from satellite image sequences using the combination of picture fuzzy clustering and spatiotemporal regression. Proceeding of Conference of GISIDEAR.Đà Nẵng,Việt Nam.

    Hoa N.D.,Son L.H. &Thong P.H. (2016). Some improvements of fuzzy clustering algorithms using picture fuzzy sets and applications for geographic data clustering.VNU Journal of Science: Computer Science and Communication Engineering. 32(3): 32-38.

    Hung W.L & Yang M.S. (2004). Similarity measures of intuitionistic fuzzy sets based on Hausdorff distance. Pattern Recognition Letters. 25: 1603-1611.

    Hung W.L.& Yang M.S. (2007). Similarity measures of intuitionistic fuzzy sets based on Lpmetric. International Journal of Approximate Reasoning. 46: 120-136.

    Hwang C.M.,Yang M.S. & Hung W.L. (2018). New similarity measures of intuitionistic fuzzy sets based on the Jaccard index with its application to clustering. International Journal of Intelligent Systems.33(8):1672-1688.

    Joshi D. &Kumar S. (2018). An Approach to Multi-criteria Decision Making Problems Using Dice Similarity Measure for Picture Fuzzy Sets. Mathematics and Computing. Springer, Singapore.

    Li D.F. & Cheng C. (2002). New similarity measures of intuitionistic fuzzy sets and application topattern recognitions. Pattern Recognition Letters. 23: 221-225.

    Liu H.W. (2005). New similarity measures between intuitionistic fuzzy sets and between elements. Mathematical and Computer Modelling. 42: 61-70.

    Mitchell H.B. (2003). On the Dengfeng-Chuntian similarity measure and its application to pattern recognition. Pattern Recognition Letters. 24: 3101-3104.

    PengX.&DaiJ. (2017). Algorithm for picture fuzzy multiple attribute decision making based on new distance measure. International Journal for Uncertainty Quantification. 7: 177-187.

    Phong P.H., Hieu D.T., Ngan R.T.H.& Them P.T. (2014). Some compositions of picture fuzzy relations, in: Proceedings of the 7th National Conferenceon Fundamental and Applied Information Technology Research, FAIR’7, Thai Nguyen. pp. 19-20.

    Singh P. (2015). Correlation coefficients for picture fuzzy sets. Journal of Intelligent & Fuzzy Systems. 28(2): 591-604.

    Son L.H. (2015).DPFCM: A novel distributed picture fuzzy clustering method on picture fuzzy sets. Expert System with Applications. 42(1): 51-66.

    Son L.H. (2016).Generalized picture distance measure and applications to picture fuzzy clustering.Applied Soft Computing. 46: 248-295.

    Son L.H. (2017).Measuring analogousness in picture fuzzy sets: from picture distance measures to picture association measures. Fuzzy Optimization and Decision Making.16(3): 359-378.

    Son L.H., Phong P.H. (2016). On the performance evaluation of intuitionistic vector similarity measures for medical diagnosis. Journal of Intelligent and Fuzzy Systems. 31: 1597-1608.

    Son L.H., Viet P.V. & Hai P.V. (2017). Picture inference system: a new fuzzy inference system on picture fuzzy set. Applied Intelligence. 46(3): 652-669.

    Szmidt E. (2014). Distances and Similarities in Intuitionistic Fuzzy Sets. Springer International Publishing Switzerland. p. 307.

    Thao N.X. &DinhN.V. (2015). Rough picture fuzzy set and picture fuzzy topologies.Journal of Computer Science and Cybernetics.31(3):245.

    ThongP.H. &Son L.H. (2014).A new approach to multi-variable fuzzy forecasting usingpicture fuzzy clustering and picture fuzzy rule interpolation method.Knowledge and Systems Engineering, Springer, Cham. pp.679-690.

    Thong P.H. (2016). Picture fuzzy clustering: a new computational intelligence method.SoftComputing. 20(9): 3549-3562.

    Thong P.H. (2016). Picture fuzzy clustering for complex data.Engineering Applications of Arti_cialIntelligence. 56: 121-130.

    Thong P. H. (2017). Some novel hybrid forecast methods based on picture fuzzy clustering for weather now-casting from satellite image sequences. Applied Intelligence. 46(1):1-15.

    Viet P.V., Chau H.T.M & Hai P.V. (2015).Some extensions of membership graphs for pictureinference systems. Seventh International Conference on Knowledge and Systems Engineering, (KSE), IEEE.pp. 192-197.

    Wei G.W. (2017). Some similarity measures for picture fuzzy sets and their applications to strategic decision making. Informatica.28:547-564.

    Wei G.W. (2017). Some cosine similarity measures for picture fuzzy sets and their applications to strategic decision making. Informatica. 28(3):547-564.

    Wei G.W. (2018). Some similarity measures for picture fuzzy sets and their applications. Iranian Journal of Fuzzy Systems.15(1):77-89.

    Wei G.W. (2018). The Generalized Dice Similarity Measures for Picture Fuzzy Sets and Their Applications. Informatica. 29(1): 107-124.

    XuZ.S.,Chen J.&Wu J.J. (2008). Clustering algorithm for intuitionistic fuzzy sets. Information Sciences.178:3775-3790.

    XuZ.S.& XiaM.M. (2010). Some new similarity measures for intuitionistic fuzzy values and their application in group decision making. Journal of System Science and Engineering. 19: 430-452.

    Ye J. (2011). Cosine similarity measures fot intuitionistic fuzzy sets and their applications. Mathematical and Computer Modelling. 53: 91-97.

    Ye J. (2012).Multicriteria decision-making method using the Dice similarity measurebased on the reduct intuitionistic fuzzy sets of interval-valued intuitionistic fuzzy sets. Applied MathematicalModelling. 36: 4466-4472.

    Ye J. (2016). Similarity measures of intuitionistic fuzzy sets based on cosine function for the decision making of mechanical design schemes. Journal of Intelligent and Fuzzy Systems. 30: 151-158.

    ZadehL.A. (1965). Fuzzy sets. Information and control. 8(3): 338-353.

    ZengS., Asharf S., ArifM. & Abdullah S. (2019). Application of Exponential Jensen Picture Fuzzy Divergence Measure in Multi-Criteria Group Decision Making. Mathematics. 7(2): 191-207.