The Evaluation of Topsis and Fuzzy-Topsis Method for Decision Making System in Data mining

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:



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.,
= , = .

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

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

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.
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