International Journal of Computer Applications (0975 – 8887)
National Conference on Advancements in Computer & Information Technology (NCACIT-2016
K-Most Demanding Products Discovery with Maximum
Expected Customers
Sofiya S. Mujawar
Dhanashree Kulkarni
Department of computer engineering,
Dr. D. Y. Patil College of Engineering,
Ambi, Talegaon.
Department of computer engineering,
Dr. D. Y. Patil College of Engineering,
Ambi, Talegaon.
ABSTRACT
Paper originates a retardant for production organizes as kmost demanding product (k-MDP). Specified a group of
customers requiring a specific variety of product with multiple
options, a group of current product of the class, a group of
candidate product that company is able to supply, and a
positive number k, it is helpful to the corporate to select k
product from the candidate product such the projected variety
of the whole customers for the k product is maximized. One
greedy algorithmic rule is implemented to look inexact
resolution for the difficulty conferred during this paper is NPhard once the amount of standards explains or options is three
or quite three. This paper dis.cover specific solution for this
issue, Apriori-Based (APR) algorithmic rule and Boundary
Pruning (UBP) algorithmic rule area unit projected. Boundary
of expected figures of total customers is additionally enforced
to look for optimum resolution of the matter. Additionally to
it, for computing least demanding product, AN algorithmic
rule is calculated to search the k-least demanding product.
This may be even helpful for production plans generation.
General Terms
Classification, Market Intelligence, Apriori algorithm, Greedy
algorithm.
Keywords
K-MDP, Decision
Discovery.
support,
Production
plan,
Product
1. INTRODUCTION
Manufacturer should have products that fulfill the needs of
consumers. As a result of the vicious group action within the
market, most are have communication with obtaining the
bigger focus of individuals. The Giant scale study goes during
this field. In such cases, client needs area unit terribly crucial.
The figure of production ways is sculptural as operate that
impacts the communication of the corporate with completely
various procurer, as an example, customers and competitors.
The problem targeted during this paper is to spot the assembly
ways with the most utility for an organization, wherever the
utility of production ways is evaluated by foretold range of the
shoppers for the popular merchandise within the set up.
Let Existing Product (EP) and Candidate Product (CP)
requirement the set of current products and thus the set of
candidate products, separately. Moreover, kCP means that the
set of k products choose from CP, cp indicates a candidate
product from kCP, and c indicates a client whose demands
square measure happy by cp. the likelihood for c selecting cp
is converse proportionate to the overall variety of products, as
well as EP and kCP, that fulfill c. Thusly; the anticipate
variety of the shoppers for cp is affected not simply by
shoppers consummated by cp a lot of the overall number of
various merchandise that satisfy a similar set of shoppers.
Given that it's attainable that the commodities in kCP can
modify each other if they satisfy a similar set of shoppers.
Consequently, no straightforward method is fastening to
obtain the set of k candidate commodities with the most vital
anticipated variety of the shoppers. Directions to administer a
skillful and effective algorithmic rule for fathom the k-MDP
finding issues square measure the aims of this paper. The
main works of this paper are: the k-MDP finding to be
associate degree increase problem of a target performs is
computed; the k-MDP looking issues are NP exhausting once
the properties for a product is three or a lot of. Greedy
algorithms square measure expected to obtain inaccurate
answers for the k-MDP looking problem; an endeavor to
obtain the optimum resolution of the matter by assessing the
higher and lower leap of the anticipated variety of the
shoppers for a collection of k candidate products for
decreasing the search area of the optimum resolution. Two
algorithms, square measure then projected to obtain the
optimum resolution of the problem by using the skinny
approaches. One algorithmic rule is designed for locating the
k-least products that is additionally required for production
arrange.
Consider the condition of the rentable house market at a town,
where the space to a hospital and to a market square measure
primary stipulations of the purchasers difficult a rentable
property. To build a promoting call, a rental company has
composed the stipulations of the space to a market and to a
hospital from the purchasers. Now assume, the rental
company claims a gaggle of attributes. The manager of the
rental company required to support to k attributes to handle
this rentable homes for rental. For gaining most profit, a way
is to induce a lot of expected range of the client for the k
chosen attributes. It’s assumed that each customer can decide
one in all the rentable homes satisfying his/her basics. At the
purpose once over one rentable house satisfies the needs of a
client, the consumer can decide one in all the attributes as per
his/her implicit preference. For the explanation for
straightforwardness, it's getting that a client can decide any
able rentable house with equal likelihood [2].
2. LITERATURE SURVEY
Many analysis has dealt the sturdy customers finding, for
instance, reverse k-nearest neighbor question [3], [4] the
inverse skyline question [5], and also the inverse top-k
question [6]. The setup of those tasks is corresponding. Given
a collection of client preferences and a specific product, the
queries assumed in [3] furnish clients whose favorite product
carries with it the particular product as shown by their
customer selections. The show of the merchandise is, during
this approach, compel to the sturdy customers.
1
International Journal of Computer Applications (0975 – 8887)
National Conference on Advancements in Computer & Information Technology (NCACIT-2016
First of all, A. Vlachou et al [6] projected a paper on converse
top-k queries. The monochromatic and dichromatic these 2
classes of reverse top-k queries are depicted. At that time, AN
formula for evaluating monochromatic reverse high k queries
is initiated, supported the geometrical attributes of the result
set. After that, they initiated an efficient threshold-based
formula (RTA) for computing dichromatic inverse high k
queries that thirstily discards candidate user decisions, while
not needed to judge the relevant top-k question. Besides, they
initiated a compartmentalization patterns depend on area
partitioning, that emerges reverse top-k views, to advance
reverse top-k question operation significantly additional. They
direct thoroughgoing check evaluations that unveil the
experience of their algorithms. RTA faithfully improves one
to orders of magnitude the naive technique. There square
{measure} variety of fascinating measure for future work. it's
wide to check in additional detail the monochromatic reverse
high k question, specifically for higher admeasurements, since
the geometrical attributes of the output set are necessary for
operational the dichromatic reverse top-k question experience.
A Reverse k-Nearest-Neighbor question searches the goals
that square measure compact by the querying object. It is
often enforced in Location-Based Services to answer
fascinating sites relevant queries. W. Wu dialect et al [7] has
supported their answers for evaluating RkNN queries on web
site knowledge. They characterize RkNN query’s fetch region
associated planned an algorithmic program referred to as
oscine to cipher it focused on the queries and a collection of
knowledge objects. Oscine is then consumed as a vicinity of
their RkNN solutions for filter and encloses the search house
for output candidates. They what is more depicted a technique
for implementing (monochromatic) RkNN algorithms to judge
dichromatic RkNN queries. Work outputs represent that the
fetch region computed by oscine encompasses a solid skinny
force and it speed the filter operation. These components
create RkNN solutions a good deal additional productive than
the present RkNN algorithms.
M. Miah et al [8] projected the matter of choosing the most
effective properties of latest tulle, specified this tuple are
hierarchal extremely, given a dataset, a question log, or both,
i.e., the tulle “emerges within the crowd”. They initiated
distinction of the difficulty for mathematician, classifying,
text and numeric information, and painted that even so the
actual fact that the difficulty is NP-complete; optimum
algorithms square measure gettable for tiny inputs. Besides,
they exhibited greedy algorithms, that square measure given
to fabricate smart inexact ratios. Whereas the issues assumed
during this paper square measure novel and important to the
vary of ad-hoc information investigation and access, they
investigate that their specific issue definition has captivity.
After that, a question log is simply associate degree
approximated substitute of real user selections, and
furthermore, during a few applications neither the info, nor
the question log is also gettable for analysis; during this
manner they needed to create presumptions concerning the
style of the group action and concerning the user selections.
At last, during this issue they were targeting preferring what
set of properties to carry off a product.
The existing algorithms for RNN question ar pertinent in
incommodious condition. Y. Tao et al [9] projected the
primary traditional approach for accessing of Associate in
nursing capricious variety of reverse nearest neighbors in
many dimensions. Despite its pertinence and obligingness,
their resolution is superior to the past techniques what is more
regarding experience and flexibility. What is more, presently
there exists no any price model for evaluating the operation
time of RNN techniques. A desirable heading for future work
is to manage the conferred approach to many diversity of
RNN measures. The improvement of such a model won't
encourage question step-up, nonetheless would possibly
likewise uncover new issues characteristics that would prompt
significantly faster algorithms.
S. Borzsonyi et al [10] bestowed however an info system may
be outstretching therefore on reckon the skyline of a
collection of concentrations. The Skyline operation is useful
for many info applications, combining call support and mental
image. Their investigate outputs painted that a info system got
to build a block-nested-loops algorithmic program
permanently cases and a divide-and conquer algorithmic
program for major cases. They bestowed the SKYLINE OF
condition as a basic extension to SQL’s choose statement,
unveil and beta valuate ex gratia algorithms to reckon the
Skyline, observe however inventory may be utilized to assist
the Skyline performance, and shown however the Skyline
operation cooperates with different question operators that's
be part of and high N. All the a lot of specifically, they
bestowed to construct a block-nested loops algorithmic
program with a window that's collected as a self-organizing
list and a divide-and-conquer algorithmic program that will
m-way separating and “Early Skyline” operations.
X. sculptor et al [11] explore the matter of shrewd the top-k
demonstrative skyline points. this can be among the primary
practices to get experience and filmable algorithms to manage
the matter. once proposing the novel skyline operator: top-k
demonstrative skyline points, they exhibit an efficient
dynamic programming based mostly formula for a 2D-space
during which a particular answer is accomplished. This live is
NP arduous for area with spatiality d ≥ three and also the
greedy heuristic for set cowl downside is promptly connected
to administer the rough inexact proportion one - 1/e. they then
developed an efficient, versatile randomized formula with a
theoretical preciseness assure.
3. PROPOSED SYSTEM
In this paper, a proposed novel technique called k-least
demanding products (k-MLP) is developed. By utilizing the
proposed system this discard the least useful products from
the list. Proposed system improves the time efficiency as this
discarding the least useful products.
4. ALGORITHM
Input: N_vector(EP, C), the set C of customer requirements,
the set CP of candidate products, and the value of k.
Output: Set of k candidate products.
1.
For each candidate product 𝐶𝑝 in CP
2.
{Compute the satisfaction bit string of 𝐶𝑝;
S = { 𝐶𝑝};
Compute 𝐸(𝑆, 𝐶);
𝑆𝐿 = < 𝐶𝑝1′ , 𝐶𝑝2′ , … , 𝐶𝑝 𝐶𝑝′ | > ;
According to the increasing order of the values
of𝐸(𝑆, 𝐶);
𝑘𝐶𝑝𝑏 = {𝐶𝑝1′ , 𝐶𝑝2′ , … , 𝐶𝑝𝑘′};
𝑏𝑎𝑠𝑒 = 𝐸(𝑘𝐶𝑝𝑏, 𝐶);
𝑘𝐶𝑝 = {𝐶𝑝1′ , 𝐶𝑝2′ , … , 𝐶𝑝𝑘 − 1′ , 𝐶𝑝𝑘 + 1′};
3.
4.
5.
6.
2
International Journal of Computer Applications (0975 – 8887)
National Conference on Advancements in Computer & Information Technology (NCACIT-2016
7.
8.
9.
𝑝𝑟𝑢𝑛𝑒 = {𝐶𝑝 𝐶𝑃 − 𝑘 + 1′ , 𝐶𝑝 𝐶𝑃 − 𝑘 +
2′ , … , 𝐶𝑝|𝐶𝑃′|}
While (true)
Compute 𝐿𝐵_𝐸2(𝑘𝐶𝑃, 𝐶);
If 𝐿𝐵_𝐸2(𝑘𝐶𝑃, 𝐶) < 𝑏𝑎𝑠𝑒
10. Compute 𝐸(𝑘𝐶𝑃, 𝐶);
If 𝐸(𝑘𝐶𝑃, 𝐶) < 𝑏𝑎𝑠𝑒
11. Base= 𝐸(𝑘𝐶𝑃, 𝐶);
12. If prune < r kCP Continue;
Else Break;
13. Return kCP = N vector(EP, C)
14. 𝑝𝑟𝑢𝑛𝑒 = {𝐶𝑝 𝐶𝑃 − 𝑘 + 1′ , 𝐶𝑝 𝐶𝑃 − 𝑘 +
2′ , … , 𝐶𝑝|𝐶𝑃′|}
15. While (true)
16. Compute 𝐿𝐵_𝐸2(𝑘𝐶𝑃, 𝐶);
Figure 1: Time Comparison
In Figure 2, Memory Comparison is depicted. Below graph
shows the memory required for K-most demanding products
and K-most demanding products after removal of K-least
product of various algorithms.
If 𝐿𝐵_𝐸2(𝑘𝐶𝑃, 𝐶) < 𝑏𝑎𝑠𝑒
17. Compute 𝐸(𝑘𝐶𝑃, 𝐶);
If 𝐸(𝑘𝐶𝑃, 𝐶) < 𝑏𝑎𝑠𝑒
18. Base= 𝐸(𝑘𝐶𝑃, 𝐶);
19. If prune < r kCP Continue;
Else Break;
20. Return kCP = N vector(EP, C)
If 𝑘𝐶𝑃𝑏 = 𝑘𝐶𝑃;
21. Else prune = kCP;
22. kCP = NextCandidateGen(SL, prune, k);
If𝑘𝐶𝑃 == Φ
Break;
23. Return kCPb;
24. Function NextCandidateGen(SL, prune, k)
While (true)
25. kCP = the next set of k candidate products
according to < t;
5. RESULTS AND DISCUSSION
In the following Figure 1, the Time Comparison is delineated.
Below graph shows the time required for K-most demanding
products and K-most demanding products after removal of Kleast product of various algorithms.
Figure 2: Memory Comparison
6. CONCLUSION
In this paper the k-MDP finding problems for looking k most
desirable product with the primary most anticipated range of
the implements is developed. The complexity is NP hard once
range of normal describers is three or quite three. 1st1 this
paper have a tendency to calculate electronic image Index
pattern, Fulfillment Bit String and N vector (EP, C), these are
provided as input to algorithms. Likewise, 2 algorithms are
designed, that are the SPG and also the immune gamma
globulin algorithmic for locating the inaccurate answer. In this
paper k-most a popular product system is employed. It
encompass four steps in 1st step the information is gathered
from the registration of the purchasers, in second step
preprocessing is completed on this knowledge to generate
electronic image index, in next step four totally different
algorithms are adapted notice the highest most popular
product. Finally this construct a web electronic looking
website for artificial dataset. While designing the propose
system, this suffer from some limitation such as it's used for
less than construction field. In future work this system will be
used for mobile industries or automobile industries. In future
system can implement the algorithm which can reduce the
time to find out the k-most demanding products and improve
the performance of the system.
7. ACKNOLEDGEMENT
The authors would like to thank the researchers as well as
publishers for making their resources available and teachers
3
International Journal of Computer Applications (0975 – 8887)
National Conference on Advancements in Computer & Information Technology (NCACIT-2016
for their guidance. We are thankful to the authorities of
Savitribai Phule University of Pune and concern members of
IJCA conference, organized by, for their constant guidelines
and support. We are also thankful to the reviewer for their
valuable suggestions. We also thank the college authorities for
providing the required infrastructure and support. Finally, we
would like to extend a heartfelt gratitude to friends and family
members.
8. REFERENCES
[1] Chen-Yi Lin, Jia-Ling Koh, and Arbee L.P. Chen,
“Determining k-Most Demanding Products with
Maximum Expected Number of Total Customers, IEEE
Transactions On Knowledge And Data Engineering, Vol.
25, No. 8, August 2013.
[2] Z. Zhang, L.V.S. Lakshmanan, and A.K.H. Tung, “On
Domination Game Analysis for Microeconomic Data
Mining,” ACM Trans. Knowledge Discovery from Data,
vol. 2, no. 4, pp. 18-EEEhowto:kopka E. Achtert, C.
Bohm, P. Kroger, P. Kunath, A. Pryakhin, and M. Renz,
“Efficient Reverse k-Nearest Neighbor Search in
Arbitrary Metric Spaces,” Proc. 25th ACM SIGMOD
Intl Conf. Management of Data, pp. 515-526, 2006.
[3] F. Korn and S. Muthukrishnan, “Influence Sets Based on
Reverse Nearest Neighbor Queries,” Proc. 19th ACM
SIGMOD Intl Conf. Management of Data, pp. 201-212,
2000.
[4] X. Lian and L. Chen, “Monochromatic and Bichromatic
Reverse Skyline Search over Uncertain Databases,” Proc.
27th ACM SIGMOD Intl Conf. Management of Data,
pp. 213-226, 2008.
IJCATM : www.ijcaonline.org
[5] A. Vlachou, C. Doulkeridis, Y. Kotidis, and K. Norvag,
“Reverse Top-k Queries,” Proc. 26th Intl Conf. Data
Eng., pp. 365-376, 2010.
[6] W. Wu, F. Yang, C.Y. Chan, and K.L. Tan, “FINCH:
Evaluating Reverse k-Nearest-Neighbor Queries on
Location Data,” Proc. 34th Intl Conf. Very Large Data
Bases, pp. 1056-1067, 2008.
[7] M. Miah, G. Das, V. Hristidis, and H. Mannila,
“Standing Out in a Crowd: Selecting Attributes for
Maximum Visibility,” Proc. 24th Intl Conf. Data Eng.,
pp. 356-365, 2008.
[8] Y. Tao, D. Papadias, and X. Lian, “Reverse kNN Search
in Arbitrary Dimensionality,” Proc. 30th Intl Conf. Very
Large Data Bases, pp. 744-755, 2004.
[9] S. Borzsonyi, D. Kossmann, and K. Stocker, “The
Skyline Operator,” Proc. 17th Intl Conf. Data Eng., pp.
421-430, 2001.
[10] X. Lin, Y. Yuan, Q. Zhang, and Y. Zhang, “Selecting
Stars: The k Most Representative Skyline Operator,”
Proc. 23rd Int’l Conf. Data Eng., pp.86-95, 2007.
[11] E. Dellis and B. Seeger, “Efficient Computation of
Reverse Skyline Queries,” Proc. 33rd Int’l Conf. Very
Large Data Bases, pp. 291-302, 2007.
[12] J. Kleinberg, C. Papadimitriou, and P. Raghavan, “A
Microeconomic View of Data Mining,” Data Mining
311-322, 1998.
4