Abstract
Significant business implications and effective handling of supply side exceptions require a successful Supplier Base Management (SBM). The process of clustering manages the number of suppliers by grouping them on the basis of similar characteristics that reduces the number of variables impacting the operations. Several existing categorical clustering techniques for such grouping contributed well than their predecessors however, the accuracy, uncertainty, entropy and computation are key measures need to be improved. Especially, the existing clustering techniques cluster randomly in case of independent and insignificant type of data. The aim of this research is to introduce a novel rough value set based categorical clustering technique named Maximum Value Attribute (MVA). The proposed MVA techniques overcome the issues of existing techniques by combining the concept of Number of Automated Clusters (NoACs) with rough value set which makes it novel and significant clustering idea. Few relevant and necessary propositions are illustrated to prove the effectiveness of NoACs. The existing and proposed rough sets based and classical categorical clustering techniques are compared in terms of purity, entropy, accuracy, rough accuracy, time and iterations. Experimental results based on a SBM and fifteen (15) benchmark data sets reveal better performance of MVA. The experimental results show significant overall percentage improvement of proposed MVA technique against existing rough based techniques for iterations (99.7%), time (99.4%), number of obtained clusters (84%), purity (32%), entropy (32%), accuracy (20%), and rough accuracy (13%).
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References
Darshit P et al (2010) A clustering algorithm for supplier base management. Int J Prod Res 48(13):3803. https://doi.org/10.1080/00207540902942891
Uddin J, Ghazali R, Deris MM, Naseem R, Shah H (2016) A survey on bug prioritization. Artif Intell Rev. https://doi.org/10.1007/s10462-016-9478-6
Naseem R, Maqbool O, Muhammad S (2013) Cooperative clustering for software modularization. J Syst Softw 86(8):2045. https://doi.org/10.1016/j.jss.2013.03.080
Wong KP, Feng D, Meikle SR, Fulham MJ (2000) Segmentation of dynamic PET images using cluster analysis. IEEE Symp Nuclear Sci 3:126. https://doi.org/10.1109/NSSMIC.2000.949251
Shuanhu W et al (2004) Cluster analysis of gene expression data based on self-splitting and merging competitive learning. IEEE Trans Inf Technol Biomed 8(1):5. https://doi.org/10.1109/TITB.2004.824724
Huang H, Meng F, Zhou S, Jiang F, Manogaran G (2019) Brain image segmentation based on FCM clustering algorithm and rough set. IEEE Access 7:12386. https://doi.org/10.1109/ACCESS.2019.2893063
Uddin J, Ghazali R, Deris MM (2017) An empirical analysis of rough set categorical clustering techniques. PLoS ONE 12(1):1. https://doi.org/10.1371/journal.pone.0164803
Gibson D, Kleinberg J (2000) Clustering categorical data: an approach based on dynamical systems. VLDB J 8:222
Ganti V, Ramakrishnan JGR (1999) In: Proceedings of the 5th ACM SIGKDD international conference on knowledge discovery and data mining, pp 73–83
Guha RKS, Rastogi S (1999) In: Proceedings 15th international conference on data engineering, pp 512–521. https://doi.org/10.1109/ICDE.1999.754967
Huang Z (1998) Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Min Knowl Disc 2:283
Herawan T, Deris MM, Abawajy JH (2010) A rough set approach for selecting clustering attribute. Knowl-Based Syst 23(3):220. https://doi.org/10.1016/j.knosys.2009.12.003
Kim DW, Lee KH, Lee D (2004) Fuzzy clustering of categorical data using fuzzy centroids. Pattern Recogn Lett 25(11):1263. https://doi.org/10.1016/j.patrec.2004.04.004
Mazlack LJ, He A, Zhu Y (2000) In: Proceedings of the ISCA 13th, international conference, CAINE, pp 1–6
Parmar D, Wu T, Blackhurst J (2007) MMR: an algorithm for clustering categorical data using rough set theory. Data Knowl Eng 63(3):879. https://doi.org/10.1016/j.datak.2007.05.005
Herawan T, Deris MM (2009) A framework on rough set-based partitioning attribute selection, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5755 LNAI, 91. https://doi.org/10.1007/978-3-642-04020-7_11
Hassanein W, Elmelegy A (2013) An algorithm for selecting clustering attribute using significance of attributes. Int J Database Theory Appl 6(5):53. https://doi.org/10.14257/ijdta.2013.6.5.06
Park IK, Choi GS (2015) Rough set approach for clustering categorical data using information-theoretic dependency measure. Inf Syst 48:289. https://doi.org/10.1016/j.is.2014.06.008
Wu J, Xiong H, Chen J (2009) Adapting the right measures for K-means clustering. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining–KDD’09, p 877. https://doi.org/10.1145/1557019.1557115. http://portal.acm.org/citation.cfm?doid=1557019.1557115
Pawlak Z (1996) In: Proceedings of Asian fuzzy systems symposium on soft computing in intelligent systems and information processing. IEEE, pp 1–6. https://doi.org/10.1109/AFSS.1996.583540. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=583540http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=583540
Davey J, Burd E (2000) In: Proceedings of 7th working conference on reverse engineering. IEEE Comput. Soc, pp 268–276. https://doi.org/10.1109/WCRE.2000.891478. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=891478
Wu J, Hassan AE, Holt RC (2005) In: IEEE international conference on software maintenance, ICSM, 2005, pp 525–535. https://doi.org/10.1109/ICSM.2005.31
Mehdizadeh E (2009) A fuzzy clustering pso algorithm for supplier base management. Int J Manag Sci Eng Manag 4(4):311. https://doi.org/10.1080/17509653.2009.10671084
Krause DR, Handfield RB, Scannell TV (1998) An empirical investigation of supplier development: reactive and strategic processes. J Oper Manag 17(1):39. https://doi.org/10.1016/S0272-6963(98)00030-8
Akman G (2015) Evaluating suppliers to include green supplier development programs via fuzzy c-means and VIKOR methods. Comput Ind Eng 86:69. https://doi.org/10.1016/j.cie.2014.10.013
Badi I, Pamucar D (2020) Supplier selection for steelmaking company by using combined Grey–Marcos methods. Decision Making Appl Manag Eng 3(2):37. https://doi.org/10.31181/dmame2003037b
Chattopadhyay R, Chakraborty S, Chakraborty S (2020) An integrated D-MARCOS method for supplier selection in an iron and steel industry. Decision Making Appl Manag Eng 3(2):49. https://doi.org/10.31181/dmame2003049c
Lu J, Zhao Z (2008) Improved TOPSIS based on rough set theory for selection of suppliers. In: 2008 International conference on wireless communications, networking and mobile computing, WiCOM 2008, pp 1–4. https://doi.org/10.1109/WiCom.2008.1537
Matić B, Jovanović S, Das DK, Zavadskas EK, Stević Z, Sremac S, Marinković M (2019) A new hybrid MCDM model: sustainable supplier selection in a construction company. Symmetry. https://doi.org/10.3390/sym11030353
Chatterjee K, Pamucar D, Zavadskas EK (2018) Evaluating the performance of suppliers based on using the R’AMATEL-MAIRCA method for green supply chain implementation in electronics industry. J Clean Prod 184(February):101. https://doi.org/10.1016/j.jclepro.2018.02.186
Đalić I, Stević Ž, Karamasa C, Puška A (2020) A novel integrated fuzzy PIPRECIA-interval rough saw model: green supplier selection. Decision Making Appl Manag Eng 3(1):80. https://doi.org/10.31181/dmame2003114d
Herawan T, Tri I, Yanto R, Deris MMAT (2010) ROSMAN : ROugh Set approach for clustering Supplier base MANagement. Biomed Soft Comput Hum Sci 16(2):105
Guha S, Meyerson A, Mishra N, Motwani R, OCallaghan L (2003) Clustering data streams: theory and practice. IEEE Trans Knowl Data Eng 15(3):515. https://doi.org/10.1109/TKDE.2003.1198387
Akay Ö, Yüksel G (2018) Clustering the mixed panel dataset using Gower’s distance and k-prototypes algorithms. Commun Stat Simul Comput 47(10):3031. https://doi.org/10.1080/03610918.2017.1367806
He Z (2004) In: Proceedings of the WAIM conference
Krishnapuram R, Keller JM (1993) A possibilistic approach to clustering. IEEE Trans Fuzzy Syst 1(2):98. https://doi.org/10.1109/91.227387
Pawlak Z et al (1995) Rough sets. Commun ACM 38(11):88. https://doi.org/10.1145/219717.219791
Yao YY (1998) Constructive and algebraic methods of the theory of rough sets. Inf Sci 109(1–4):21. https://doi.org/10.1016/S0020-0255(98)00012-7
Bonikowski Z, Bryniarski E, Wybraniec-Skardowska U (1998) Extensions and intentions in the rough set theory. J Inform Sci 107(1–4):149. https://doi.org/10.1016/S0020-0255(97)10046-9
Ali MI, Davvaz B, Shabir M (2013) Some properties of generalized rough sets. Inf Sci 224:170. https://doi.org/10.1016/j.ins.2012.10.026
Wei W, Liang J (2019) Information fusion in rough set theory: an overview. Inf Fusion 48(January 2018):107. https://doi.org/10.1016/j.inffus.2018.08.007
Pamucar D (2020) The application of the hybrid interval rough weighted power-Heronian operator in multi-criteria decision-making. Oper Res Eng Sci Theory Appl 3(2):54. https://doi.org/10.31181/oresta2003049p
Pawlak Z, Skowron A (2007) Rudiments of rough sets. Inf Sci 177(1):3. https://doi.org/10.1016/j.ins.2006.06.003
Kumar P, Tripathy B (2009) MMeR an algorithm for clustering heterogeneous data using rough set theory. Int J Rapid Manuf 1(2)
Yanto I, Herawan T, Deris M (2011) Data clustering using variable precision rough set. Intell Data Anal 15:465. https://doi.org/10.3233/IDA-2011-0478
Tripathy B, Ghosh A (2011) SDR: an algorithm for clustering categorical data using rough set theory. IEEE Recent Adv Intell Comput Syst. https://doi.org/10.1109/RAICS.2011.6069433
Jyoti (2013) Clustering categorical data using rough st: a review. Int J Adv Res IT Eng 2(12):30
Park IK, Choi GS (2015) A variable-precision information-entropy rough set approach for job searching. Inf Syst 48:279. https://doi.org/10.1016/j.is.2014.05.012
Yanto ITR, Ismail MA, Herawan T (2016) A modified Fuzzy k-Partition based on indiscernibility relation for categorical data clustering. Eng Appl Artif Intell 53:41. https://doi.org/10.1016/j.engappai.2016.01.026
Tripathy BK, Goyal A, Sourav PA (2016) A comparative analysis of rough intuitionistic fuzzy k-mode algorithm for clustering categorical data. Res J Pharm Biol Chem Sci 7(5):2787
Tripathy B, Goyal A, Chowdhury R, Sourav PA (2017) MMeMeR: an algorithm for clustering heterogeneous data using rough set theory. Int J Intell Syst Appl 8:25. https://doi.org/10.5815/ijisa.2017.08.03
Tan PN, Steinbach M, Kumar V (2006) Introduction to data mining. Addison-Wesley. https://doi.org/10.1016/0022-4405(81)90007-8. http://www-users.cs.umn.edu/~kumar/
Garcia HV, Shihab E (2014) In: Proceedings of the 11th working conference on mining software repositories, pp 72–81
Christopher PR, Manning D, Schütze H (2009) Introduction to information retrieval. Cambridge University Press
Maqbool O, Babri HA (2007) Hierarchical clustering for software architecture recovery. IEEE Trans Softw Eng 33(11):759. https://doi.org/10.1109/TSE.2007.70732
Wang Y, Liu P, Guo H, Li H, Chen X (2010) In: International conference on intelligent computing and cognitive informatics, pp 1–4. https://doi.org/10.1109/ICICCI.2010.45
Rissino S, Lambert-torres G (2009). In: Julio P, Adem K (eds) Data mining and knowledge discovery in real life applications. Austria, I-Tech, Vienna, pp 35–58
Grzymala-busse JW (2005) Rough set theory with applications to data mining. Real World Appl Comput Intell 179:221
Zhao Y (2001) Criterion functions for document clustering: experiments and analysis. Tech. rep., Department of Computer Science, University of Minnesota, USA
Aggarwal C, Reddy C (2014) Data clustering: algorithms and applications. CRC Press Taylor & Francis Group,
Amigó E, Gonzalo J, Artiles J, Verdejo F (2009) A comparison of extrinsic clustering evaluation metrics based on formal constraints. Inf Retrieval 12(4):461. https://doi.org/10.1007/s10791-008-9066-8
Li T, Ogihara M (2004) In: Proceedings of the 21st international conference on machine learning, Banff, Canada
Beaubouef T, Petry FE, Arora G (1998) Information-theoretic measures of uncertainty for rough sets and rough relational databases. J Inf Sci 5
MacQueen JB (1967) K means some methods for classification and analysis of multivariate observations. In: 5th Berkeley symposium on mathematical statistics and probability, vol 1(233), p 281. http://projecteuclid.org/euclid.bsmsp/1200512992
Ahmad A, Dey L (2007) A k-mean clustering algorithm for mixed numeric and categorical data. Data Knowl Eng 63(2):503. https://doi.org/10.1016/j.datak.2007.03.016
Dua D, Graff C 2017, UCI Machine Learning Repository. http://archive.ics.uci.edu/ml
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Uddin, J., Ghazali, R., Deris, M.M. et al. A novel rough value set categorical clustering technique for supplier base management. Computing 103, 2061–2091 (2021). https://doi.org/10.1007/s00607-021-00950-w
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DOI: https://doi.org/10.1007/s00607-021-00950-w