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The Evaluation of Topsis and Fuzzy-Topsis Method for Decision Making System in Data mining
1.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 03 Issue: 09 | Sep-2016 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1027 The Evaluation of Topsis and Fuzzy-Topsis Method for Decision Making System in Data mining R. Dharmarajan1, C.Sharmila mary2 1 Assistant Professor, Department of Computer Science 2 Research Scholar Department of Computer Science Thanthai Hans Roever College, Perambalur-621212,Tamil Nadu ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Due to the growing competition of globalization and fast technological improvements, world markets demand companies to have quality and professional human resources. This can only be achieved by employing potentially adequate personnel. This research presents the fuzzy TOPSIS as the analytical tool that determines the weights of each criterion. Fuzzy theory provides a proper tool to encounter with uncertainties and complex environment. The purpose of this paper is to use the fuzzy TOPSIS method based on fuzzy sets. Key Words: Data mining, Decision Making, Fuzzy-Topsis, Topsis. 1. INTRODUCTION The TOPSIS method was first developed by Hwang and Yoon (Hwang & Yoon, 1981) and ranks the alternatives according to their distances from the ideal and the negative ideal solution, i.e. the best alternativehassimultaneouslythe shortest distance from the ideal solution and the farthest distance from the negative ideal solution. The ideal solution is identified with a hypothetical alternative that has the best values for all considered criteria whereas the negative ideal solution is identified with a hypothetical alternativethathas the worst criteria values. In practice, TOPSIS has been successfully applied to solve selection/evaluation problems with a finite number of alternatives [1] because itisintuitive and easy to understand and implement. Furthermore, TOPSIS has a sound logic that represents the rationale of human choice [2] and has been proved to be one of the best methods in addressing the issue of rank reversal. In this paper we extended TOPSIS for KM strategies selection problem because of following reasons and advantages as Shih and his co-operators did for consultant selection problem [3]. A sound logic that represents the rational of human choice. A scalar value that accounts for both the best and worst alternative simultaneously. A simple computationprocessthatcanbeeasily programmed into a spreadsheet. The performance measures of all alternatives on attributes can be visualizedona polyhedron, at least for any two dimensions. 2. TOPSIS METHOD A positive ideal solution maximizes the benefit criteria or attributes and minimizes the cost criteria or attributes, whereas a negative ideal solutionmaximizesthecostcriteria or attributes and minimizes the benefit criteria or attributes [3]. The TOPSIS method is expressed in a succession of six steps as follows: Step 1: Calculate the normalized decision matrix. The normalized value ijr is calculated as follows: m i ijijij xxr 1 2 i =1, 2, ..., m and j = 1, 2, ..., n. Step 2: Calculate the weighted normalized decision matrix. The weighted normalized value is calculated as follows: wrv jijij i =1, 2,..., m and j = 1, 2, ..., n. (1) where wj is the weight of the j th criterion or attribute and n j jw1 1. Step 3: Determine the ideal ( A * ) and negative ideal ( A ) solutions. },...,2,1|{)}|min(),|max{( ** mjjj vCvCvA jcijibiji (2) },...,2,1|{)}|max(),|min{( mjjj vCvCvA jcijibiji (3) Step 4: Calculate the separation measures using the m- dimensional Euclidean distance. Theseparationmeasuresof each alternative from the positive ideal solution and the negative ideal solution, respectively, are as follows:
2.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 03 Issue: 09 | Sep-2016 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1028 m j jiji mjvvS 1 2** ,...,2,1,)( (4) m j jiji mjvvS 1 2 ,...,2,1,)( (5) Step 5: Calculate the relative closeness to the ideal solution. The relative closeness of the alternative Ai with respect to A * is defined as follows: mi SS S RC ii i i ,...,2,1,* * (6) Step 6: Rank the preference order. 3. FUZZY TOPSIS MODEL The technique called fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Situation) can be used to evaluate multiple alternatives against the selected criteria. In the TOPSIS approach an alternative that is nearest to the Fuzzy Positive Ideal Solution (FPIS) and farthest from the Fuzzy Negative Ideal Solution (FNIS) is chosen as optimal. An FPIS is composed of the best performance values for each alternative whereas the FNIS consists of the worst performance values. A detailed description and treatment of TOPSIS is discussed by (TJ, J3) and we have adapted the relevant steps of fuzzy TOPSIS as presented below. Fig -1: Flow chart of the proposed fuzzy method The steps of the fuzzy TOPSIS method are following Step1: In general [18], a typical fuzzy multiple attribute group decision-making problem could be concisely constructed in matrix format as Where A1,A2,…, Am are possible alternatives to be selected, X1,X2…, Xn denote the evaluation attributes which measure the performance of alternatives, represents the fuzzy performance rating of the ith alternative Ai versus the jth attribute Xj and is the weight of attribute Xj . In this paper, ; and = 1; 2; : : : ; n are assessed in linguistic terms described by triangular fuzzy numbers, i.e., = , = .
3.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 03 Issue: 09 | Sep-2016 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1029 Step2: A group of k experts is established to consider and evaluate the importance weights of the attributes. Supposed that members of the decision group are as follows In addition, different voting power weights are assigned to each group member according to their professional titles, given by Where expressed by triangular fuzzy number represents the voting power weight of the tth decision maker. Step3: The fuzzy collective opinion matrix for all experts can be expressed as Where indicates the fuzzy weight of the jth attribute assessed by the tth evaluator. Fig-2. Fuzzy membership function of the linguistic scale Step4: To integrate all the expert opinions, the following equation is adopted to aggregate the subjective judgements of k experts for obtaining the fuzzy weight of attribute Xj. Step5: The normalization of fuzzy decision matrix is performed by applying the linear scale transformation method since it preserves the property that the values of converted triangular fuzzy numbers are within the range [0, 1]. Hence, the normalized fuzzy decision matrix denoted by could be identified as Where is associated with benefit attributes and is associated with cost attributes. Step6: The weighted normalized fuzzy decision matrix can be computed by multiplying the normalized fuzzy decision element and the aggregative fuzzy weight of each attribute, which is defined as Where and are positive triangular fuzzy numbers. Step. The fuzzy positive ideal solution (FPIS, ) and fuzzy negative ideal solution (FNIS, ) can be determined as
4.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 03 Issue: 09 | Sep-2016 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1030 Considering that the ranges of decision elements belong to the closed interval [0, 1], it satisfies that and (0; 0; 0) where is associated with benefit attributes and is associated with cost attributes. Step8.: The Euclidean distance method is applied to derive the distance of each alternative from and respectively as Where denotes the distance measurement between two triangular fuzzy numbers and . Step9: Once the and of each alternative have been calculated successfully, a closeness coefficient is defined to determine the final ranking order of all alternatives which is calculated as It is obvious that the alternative is closer to and farther from as approaches to 1. Therefore, the ranking order of all alternatives can be obtained according to their closeness coefficients [18]. 4. LITERATURE REVIEW Technique for Order Performance by similarity to Ideal solution (TOPSIS), one of the most classical methods for solving MCDM problem, was first developed by Hwang and Yoon [5]. It is based on the principle that the chosen alternative should have the longest distance from the negative-ideal solution i.e. the solution that maximizes the cost criteria and minimizes the benefits criteria; and the shortest distance from the positive-ideal solution i.e. the solution that maximizes the benefit criteria and minimizes the cost criteria. In classical TOPSIS the rating and weight of the criteria are known precisely. However, under many real situations crisp data are inadequate to model real life situation since human judgments are vague and cannot be estimated with exact numeric values [5]. To resolve the ambiguity frequently arising in information from human judgments fuzzy set theory has been incorporated in many MCDM methods including TOPSIS. In fuzzy TOPSIS all the ratings and weights are defined by means of linguistic variables. A number of fuzzy TOPSIS methods and applications have been developed in recent years. Chen and Hwang [6] first applied fuzzy numbers to establish fuzzy TOPSIS. Triantaphyllou and Lin [15] developed a fuzzy TOPSIS method in which relative closeness for each alternative is evaluated based on fuzzy arithmetic operations. Liang [13] proposed Fuzzy MCDM based on ideal and anti-ideal concepts. Chen [11] considered triangular fuzzy numbers and defined crisp Euclidean distance between two fuzzy numbers to extend the TOPSIS method to fuzzy GDM situations. Chen and Tsao [8] are to extend the TOPSIS method based on Interval-valued fuzzy sets in decision analysis. Jahanshahloo et al. [12] and Chu and Lin [9] extended the fuzzy TOPSIS method based on alpha level sets with interval arithmetic. Chen and Lee [7] extended fuzzy TOPSIS based on type-2 fuzzy TOPSIS method in order to provide additional degree of freedom to represent the uncertainties and fuzziness of the real world. Fuzzy TOPSIS has been introduced for various multi-attribute decision-making problems. Yong [14] used fuzzy TOPSIS for plant location selection and Chen et al. [10] used fuzzy TOPSIS for supplier selection. Kahraman et al. [17] utilized fuzzy TOPSIS for industrial robotic system selection. Wang and Chang [16] applied fuzzy TOPSIS to help the Air Force Academy in Taiwan choose optimal initial training. 5. CONCLUSION The expanding competitiveness due to the globalization has dramatically increased the need for
5.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 03 Issue: 09 | Sep-2016 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1031 manufacturers to produce high-quality products efficiently and respond to changes quickly. Flexible manufacturing systems provide the means to arrive at a solution consistent with industrial goals and objectives. To help address the issue of evaluation and selection of alternative FMSs where the information available is subjective and imprecise, an effective fuzzy- TOPSIS method applied in the group decision-making model is developed. This model is intended to enhance group decision-making, promote consensus and provide invaluable analysis aids. The paper presents study explored the use of TOPSIS and fuzzy TOPSIS method. REFERENCES [1] Jee, D.H., & Kang, J.K. (2000). A method for optimal material selection aided with decision making theory. Materials and Design, 21(3), 199-206. [2] Shih, H.S, Syur, H.J, & Lee, E.S. (2007). An extension of TOPSIS for group decision making. Mathematical and Computer Modeling, 45, 801-813. [3] Shih, H.S, Syur, H.J, & Lee, E.S. (2007). An extension of TOPSIS for group decision making. Mathematical and Computer Modeling, 45, 801-813. [4] https://archive.org/stream/arxiv- 1205.5098/1205.5098_djvu.txt [5] Hwang, C. L, and Yoon, K. (1981). Multiple attribute decision making methods and applications. Springer–Heidelberg, Berlin. [6] Chen, S. J., and Hwang, C. L. (1992). Fuzzy multi attribute decision making, lecture notes in economics and mathematical system series, vol. 375. Springer-Verlag New York. [7] Chen, S.M., and Lee, L.W. (2010). Fuzzy multiple attributes group decision-making based on the interval type-2 TOPSIS method. Expert Systems with Applications, Vol. 37, No. 4, pp. 2790-2798. [8] Chen, T.Y., and Tsao, C.Y. (2008). The interval- valued fuzzy TOPSIS method and experimental analysis. Fuzzy Sets and Systems, Vol. 159, No. 11, pp. 1410-1428. [9] Chu, T. C., and Lin, Y. C. (2009). An interval arithmetic based fuzzy TOPSIS model. Expert Systems with Applications,Vol.36,No.8,pp.10870- 10876. [10] Liang, G. S. (1999). Fuzzy MCDM based on ideal and anti-ideal concepts. European Journal of Operational Research, Vol.112,No.3,pp.682-691. [11] Chen, C. T. (2000). Extension of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems, Vol. 114, No. 1, pp. 1-9. [12] Jahanshahloo, G. R., Hosseinzadeh Lotfi, F., and Izadikhah, M. (2006). Extension of the TOPSIS method for decision-making problems with fuzzy data. Applied Mathematics and Computation, Vol.181, No. 2, pp. 1544–1551. [13] Liang, G. S. (1999). Fuzzy MCDM based on ideal and anti-ideal concepts. European Journal of Operational Research, Vol.112,No.3,pp.682-691. [14] Yong, D. (2006). Plant location selection based on fuzzy TOPSIS. International Journal of Advanced Manufacturing Technologies, Vol. 28, No. 7-8, pp. 323-326. [15] Triantaphyllou, E., and Lin, C.L. (1996). Development and evaluation of five fuzzy multi attribute decision making methods. International Journal of Approximate Reasoning, Vol. 14, No. 4, pp. 281–310. [16] Wang, T. C., and Chang, T. H. (2007).Applicationof TOPSIS in evaluating initial trainingaircraftunder a fuzzy environment. Expert Systems with Applications, Vol. 33, No. 4, pp. 870-880. [17] Kahraman, C., Cevik, S., Ates, N. Y., and Gulbay, M. (2007). Fuzzy multi-criteria evaluation of industrial roboticsystems.Computers&Industrial Engineering, Vol. 52, No. 4, pp. 414-433. [18] Shanliang Yang, Ge Li, Kedi Huang "Group Decision-Making Model using Fuzzy-TOPSIS Method for FMS Evaluation" ISBN: 978-960-474- 383-4, Advances in Automatic Control,June 11, 2011.
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