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2021年1月21日发(作者:sweater什么意思)

有关零售超市数据毕业设计中
英文翻译




毕业设计

论文


外文翻译













对零售超市数据进行最

优产品选择的数据挖掘框

架:广义
PROFSET
模型













网络工程
























041

























朱朋伟














指导教师
























英文原文











2008










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外文翻译

附录

英文原文

A Data Mining Framework for Optimal
Product Selection in Retail Supermarket Data:
The Generalized PROFSET Model

1 Introduction
Since almost all mid to large size
retailers today possess electronic sales
transaction Systems, retailers realize that
competitive advantage will no longer be
achieved by the mere use of these systems
for purposes of inventory management or
facilitating customer check-out. In contrast,
competitive advantage will be gained by
those retailers who are able to extract the
knowledge hidden in the data, generated by
those systems, and use it to optimize their
marketing decision making. In this context,
knowledge about how customers are using
the retail store is of critical importance and
distinctive competencies will be built by
those retailers who best succeed in
extracting actionable knowledge from these

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data. Association rule mining
[2]
can help
retailers to efficiently extract this
knowledge from large retail databases. We
assume some familiarity with the basic
notions of association rule mining.

In recent years, a lot of effort in the area
of retail market basket analysis has been
invested in the development of techniques to
increase the interestingness of association
rules. Currently, in essence three different
research tracks to study the interestingness
of association rules can be distinguished.

First, a number of objective measures of
interestingness have been developed in
order to filter out non- interesting
association rules based on a number of
statistical properties of the rules, such as
support and confidence
[2],
interest
[14]
,
intensity of implication
[7]
, J-measure
[15]
,
and correlation
[12]
. Other measures are
based on the syntactical properties of the
rules
[11]
, or they are used to discover the
least-redundant set of rules
[4]
. Second, it
was recognized that domain knowledge may
also play an important role in determining
the interestingness of association rules.
Therefore, a number of subjective measures

3
外文翻译

of interestingness have been put forward,
such as unexpectedness
[13]
, action ability
[1]

and rule templates
[10]
. Finally, the most
recent stream of research advocates the
evaluation of the interestingness of
associations in the light of the
micro-economic framework of the retailer
[9].

More specifically, a pattern in the data is
considered interesting only to the extent in
which it can be used in the decision-making
process of the enterprise to increase its
utility.

It is in this latter stream of research that
the authors have previously developed a
model for product selection called
PROFSET
[3]
, that takes into account both
quantitative and qualitative elements of
retail domain knowledge in order to
determine the set of products that yields
maximum cross-selling profits. The key idea
of the model is that products should not be
selected based on their individual
profitability, but rather on the total
profitability that they generate, including
profits from cross-selling. However, in its
previous form, one major drawback of the
model was its inability to deal with

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supermarket data (i.e., large baskets). To
overcome this limitation, in this paper we
will propose an important generalization of
the existing PROFSET model that will
effectively deal with large baskets.
Furthermore, we generalize the model to
include category management principles
specified by the retailer in order to make
the output of the model even more realistic.

The remainder of the paper is organized
as follows. In Section 2 we will focus on the
limitations of the previous PROFSET model
for product selection. In Section 3, we will
introduce the generalized PROFSET model.
Section 4 will be devoted to the empirical
implementation of the model and its results
on real-world supermarket data. Finally,
Section 5 will be reserved for conclusions
and further research.

2 The PROFSET Model
The key idea of the PROFSET model is
that when evaluating the business value of a
product, one should not only look at the
individual profits generated by that product
(the naive approach), but one must also

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take into account the profits due to
cross-selling effects with other products in
the assortment. Therefore, to evaluate
product profitability, it is essential to look
at frequent sets rather than at individual
product items since the former represent
frequently co-occurring product
combinations in the market baskets of the
customer. As was also stressed by Cabena et
al.
[5]
, one disadvantage of associations
discovery is that there is no provision for
taking into account the business value of an
association. The PROFSET model was a
first attempt to solve this problem. Indeed,
in terms of the associations discovered, the
sale of an expensive bottle of wine with
oysters accounts for as much as the sale of a
carton of milk with cereal. This example
illustrates that, when evaluating the
interestingness of associations, the
micro-economic framework of the retailer
should be incorporated. PROFSET was
developed to maximize cross-selling
opportunities by evaluating the profit
margin generated per frequent set of
products, rather than per product. In the
next Section we will discuss the limitations

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of the previous PROFSET model. More
details can be found elsewhere
[3]
.
2.1 Limitations
The previous PROFSET model was
specifically developed for market basket
data from automated convenience stores.
Data sets of this origin are characterized by
small market baskets (size 2 or 3) because
customers typically do not purchase many
items during a single shopping visit.
Therefore, the profit margin generated per
frequent purchase combination (X) could
accurately be approximated by adding the
profit margins of the market baskets (Tj)
containing the same set of items, i.e. X = Tj.
However, for supermarket data, the existing
formulation of the PROFSET model poses
significant problems since the size of
market baskets typically exceeds the size of
frequent item sets. Indeed, in supermarket
data, frequent item sets mostly do not
contain more than 7 different products,
whereas the size of the average market
basket is typically 10 to 15. As a result, the
existing profit allocation heuristic cannot be
used anymore since it would cause the

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外文翻译

model to heavily underestimate the profit
potential from cross-selling effects between
products. However, getting rid of this
heuristic is not trivial and it will be
discussed in detail in Section 3.1.

A second limitation of the existing
PROFSET model relates to principles of
category management. Indeed, there is an
increasing trend in retailing to manage
product categories as separate strategic
business units
[6]
. In other words, because of
the trend to offer more products, retailers
can no longer evaluate and manage each
product individually. Instead, they define
product categories and define marketing
actions (such as promotions or store layout)
on the level of these categories. The
generalized PROFSET model takes this
domain knowledge into account and
therefore offers the retailer the ability to
specify product categories and place
restrictions on them.

3 The Generalized PROFSET Model
In this section, we will highlight the
improvements being made to the previous

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龟裂的意思-


龟裂的意思-


龟裂的意思-



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