Soft Lattice in Approximation Space

International Journal of Trend in Scientific Research and Development (IJTSRD)
Volume 6 Issue 6, September-October 2022 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470
@ IJTSRD | Unique Paper ID – IJTSRD52246 | Volume – 6 | Issue – 6 | September-October 2022 Page 2106
Soft Lattice in Approximation Space
Payoja Mohanty
3270 Klaiman Drive, Mississauga, Ontario, Canada
ABSTRACT
Rough set theory is a powerful tool to analysis the uncertain and
imprecise problem in information systems. Also the soft set and
lattice theory can be used as a general mathematical tool for dealing
with uncertainty. In this paper, we present a new concept, soft rough
lattice where the lower and upper approximations are the sub lattices
and narrate some properties of soft rough lattice with some examples.
KEYWORDS: Rough set, soft set, lattice theory, lower approximation
and upper approximation of soft lattice
How to cite this paper: Payoja Mohanty
"Soft Lattice in Approximation Space"
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International Journal
of Trend in
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Issue-6, October
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International Journal of Trend in
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1. INTRODUCTION
Rough set theory was proposed by Prof. Z. Pawlak in
1982[7] to deal with uncertainty, vagueness and
imprecise problems. The classical rough set theory is
based on an equivalence relation that is, a knowledge
on a universe of objects U. Knowledge is based on
the ability to classify objects, and by objects we mean
anything we can think of, for example real things,
states, abstract concepts, process, moment of time etc.
For a finite set of objects U, any subset X ⊆ U will be
called a concept or a category in U and any family of
concepts in U will be referred to as knowledge about
U. Let us consider a classification or partition of a
certain universe U, that is, a family
X={ } is called a partition of U if
for , i≠j, i,j=1,2,…,n and
= U. W know that partition(or classification) of
U and an equivalence relation on U are
interchangeable notion. So we consider equivalence
relation R: U→ U instead of classification as it is easy
to manifulate.
Let R be an equivalence relation on universe U, then
U/R be the family of all equivalence classes of R
referred to as categories or concepts of R, and
denotes a category in R containing an element
x∈ U. Here R is a knowledge on U.
In 1999 D. Molodtsov[6] introduced the concept of
soft sets, which is a new mathematical tool for
dealing with uncertainty. Soft set theory has potential
applications in many different fields including game
theory, operational research, probability theory. Maji
et al[4] defined several operations on soft sets and
made a theoretical study on the theory of soft sets.
The combination of the theories soft set and rough set
be studied by Das and Mohanty[2], Mohanty et al[5]
and also by the other authors Feng et al[3].
Lattice are relatively simple structures since the basic
concepts of the theory include onlyorders, least upper
bounds, greatest lower bounds. Now the lattice plays
on important role in many disciplines of computer
sciences and engineering. Though the concept of
lattice was introduced by Pirce and Schroder, but
Boole(1930) and Birkhoff(1967)[1] gave the actual
development of lattice theory.
2. PRELIMINARIES:
Definition-2.1:[8] Let U be the universal set and C be
an equivalence relation(or knowledge) on U, where C
is termed as indiscernibility relation. U/C be family of
all equivalence class of C, known as catagories of C,
for x U. is an equivalence class of x. The
relational system K=(U,C) is called a approximation
IJTSRD52246

International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470
space. The lower approximation and upper
approximation of a set X U under the indiscernibility
relation C are defined as
X={x U: } and
X={ x U: } respectively.
Example-2.2:
Let U={ } be the various colors for a
painting. Let C be the knowledge on U, we get a
partition of U as
U/C={{ }, { }, { }, { }, { }}
Where are red color, are green color,
are yellow color, color and is
blue color.
Let X={ }⊂ U. That is X certain painting the
lower and upper approximation of X are
X={ }. So yellow color certainly belong to the
painting X.
And X={ , , }. So green and yellow color
are possibly used for the painting belong to X.
Hence, X≠ X.
Therefore, the set X is rough with respect to
knowledge C.
Example-2.3:
Let U={ } be the different chocolate
in a jar according to their price. Let D be the
knowledge on U, we get a partition of U as
U/D={{ , },{ }, { }, { },
{ }}
That is { , } are the 5rupees chocolate,
{ } are the 10rupees chocolate, { } are
10rupees chocolate, { } are 30rupees chocolate
and { }is 50rupees chocolate. Let X={ , ,
}⊂ U, that is a set of chocolates are picked a
random.
The lower approximation and upper approximation of
X are
X={ }. So 20rupees and 50 rupees
chocolate are certainly belong to X.
X={ , , }. So the chocolates of 20
rupees, 30 rupees and 50rupees are possibly classified
belong to X.
Hence, X≠ X.
Therefore, the set X is rough with respect to
knowledge D.
Definition-2.4: Let U be an initial universe, E be the
set of parameters related to U. Let P(U) denotes the
power set of U, A⊆ E and F be a mapping given by
F:A →P(U), then the pair (F,A) is called soft set over
U.
Example-2.5: Let U={ } be the set of
shop, E={ } be the set of parameters on U
that is stands for flower, stands for seed,
stands for fresh, stands for stale and stands for
root vegetables. Let a mapping F:E→P(U) be given
as F( )={ , }, = { },
F( )={ }, F( )={ } and F( )={ }
means and are root vegetables.
Let A={ }⊆ E then the soft set
(F,A)={( , F( ) ), (green, ), (fresh,
F( ))}
={(cheap, { , }), (green, { }), (fresh,
{ })}
Example-2.6:
Let U={ } be different quality bikes
available in India market, E= { } be the set of
parameters on U that is stands for expensive,
stands for good mileage, stands for sports,
stands for crusier and stands for touring. Let a
mapping G:E→P(U) be given as G( )={ , }
the expensive bikes are , and , =
{ , }, G( )={ }, G( )={ , }
and G( )={ }.
Let A={ }⊆ E then the soft set
(G,A)={( , G( ) ), (good mileage,
), (cruiser, G( ))}
={(expensive, { , }), (good mileage,
{ , }), (cruiser, { , })}
Definition-2.7: Let Z=(Z,≾) be a partially
ordered set. Then Z is called a lattice if for any two
elements r and s of Z have a least upper bound r∨s
and a greatest lower bound r∧s are in Z.
Defintion-2.8: Let (L,∨,∧) be a lattice and let S⊆ L be
a subset of L. Then (S,∨,∧) is called a sublattice of
(L,∨,∧) if and only if S is closed under both
operations of join(∨) and meet(∧).
Example-2.9:
The power set P(S) is a lattice under the operation
union and intersection. That is S={⍺, }
Then P(S)={⌽,{ ⍺},{ },{ }, {⍺, }, { },
{⍺, },S}.
Let A=(P(S),⋃,∩) is a lattice.
That is { ⍺}⋃ { }={ }
{⍺}∩ { }=⌽
{ }⋃{ }={ }
{ }∩{ }= { }
B={T,⋃,∩} is sublattice of A, where T={⌽, {⍺},
{ }, {⍺, }}

International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470
Example-2.10: Let A=( ,|)=
(1,2,3,4,5,6,10,12,15,20,30,60) is a lattice.
Join: 6 ∨10=lcm(6,10)=30
Meet: 6∧ 10=gcd(6,10)=2
Join: 10∨12=lcm(10,12)=60
Meet: 10∧ 12=gcd(10,12)=2
B={K, |} is a sublattice of A, where A={3,6,12}.
3. SOFT ROUGH LATTICE:
Let L be an lattice, E be the set of parameter and P(L)
denotes the set of all sublattice of L. The collection
W=(F,A) is soft lattice over L, where F is a mapping
given by F:A → P(L). Then S=(L,F,A) is called soft
lattice approximation space.
Definition-3.1: For X ⊆L, we define lower and upper
approximation as
and
If , then X is called soft rough
lattice. Otherwise X is soft definable lattice. Here,
and are sublattices.
Example-3.2: Let S={⍺, } be a set and L=(S,⊆) =
{⌽,{ ⍺},{ },{ }, {⍺, }, { }, {⍺, },S} be a
lattice. Let E={ } be the set of parameters and
D={ }. Let F: D→ P(L) be a mapping
given by F( )={{⍺},{⍺, }}, F( )={ ⌽,{⍺},{ },
{⍺, }}, F( )={{⍺},{⍺, }}, F( )= {⌽,{⍺}, S},
F( )={ { }, { } } be sublattices of L. Let
X={{⍺},{⍺, }}∈ P(L). Then {{⍺},{⍺, }}
and = {⌽,{⍺},{ },{⍺, },{⍺, },S}
Theorem-3.3:
Let W=(F,A) be soft lattice over L, S=(L,F,A) be a
soft lattice approximation space and X, Y⊆ L, we
have
A.
B.
C. X⊆ Y⇒
D. X⊆ Y⇒
Proof:
A. According to definition of soft lattice lower and
upper approximation, we have
and
B. and
C. Assume that X⊆ Y
By definition, F(a)⊆ X⇒ u∈
So, u∈ F(a)⊆ Y
Therefore,
D. Suppose that X⊆ Y
⇒
⇒ ⊇
⇒ L-
⇒
Theorem-3.4:
Let W=(F,A) be soft lattice over L, S=(L,F,A) be a
soft lattice approximation space and X, Y⊆ L, we
have
⋃
(b) ∩
(c) ⊇ ⋃
(d)
(e)
(f)
Proof:
These can be prove directly.
4. CONCLUSION:
This paper aims to define soft rough lattice which is a
new mathematical model to deal with uncertain and
vague concept. Some properties are proved. Frther
research can be made for the new model soft rough
lattice.
REFERENCES
[1] G. Birkhoff, “Lattice Theory”, Trans. Amer.
Math, Soc., New York, 1967.
[2] M. Das, D. Mohanty, “Dispensability on soft
rough set theory”, International Journal of
Engineering, Science and Mathematics,
vol.10(4), pp. 1-10, 2021.
[3] F.Feng, X. Liu, V. Leoreanu-Fotea, Y. B. Jun,
Soft sets and soft rough sets, Information
Sciences 18(2011) 1125-1137.
[4] P.K. Maji, R. Biswas, R. Roy, “Soft set
theory”, Computers and Mathematics with
Applications, vol.45, pp. 555-562, 2003.
[5] Mohanty, N. Kalia, L. Pattanayak, B.B. Nayak,
“An introduction to rough soft set”,
Mathematical Science, International Research
Journal, vol. 1(3), pp. 927-936, 2012.
[6] Molodtsov, “Soft set theory first results”,
Computers and Mathematical with
Applications, vol. 37, pp. 19-31, 1999.
[7] Z. Pawlak, “Rough set theory”, International
Journal of Computer and Information Sciences,
vol. 11, pp. 341-356, 1982.
[8] Z. Pawlak, “Rough sets-theoretical aspects of
reasoning about data”, Kluwer Academic
Publishers, 1991.

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