有關零售超市畢業(yè)設計外文翻譯

1、英文原文 畢業(yè)設計（論文）外文翻譯題 目 對零售超市數(shù)據(jù)進行最優(yōu)產品選擇的數(shù)據(jù)挖掘框架：廣義PROFSET模型 專 業(yè) 網絡工程 1外文翻譯附錄英文原文A Data Mining Framework for OptimalProduct Selection in Retail Supermarket Data:The Generalized PROFSET Model1 IntroductionSince almost all mid to large size retailers today possess electronic sales transaction Systems, reta

2、ilers 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,

3、 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 f

4、rom these 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

5、 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 t

6、o 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 us

7、ed 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 of interestingness have been put forward, such as unexpectedness 13,

8、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

9、 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 reta

10、il 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-s

11、elling. However, in its previous form, one major drawback of the model was its inability to deal with 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 bas

12、kets. 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

13、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 ModelThe

14、 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 take into account the profits due to cross-selling effects with other products in the assortmen

15、t. 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 o

16、f 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

17、 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 generat

18、ed per frequent set of products, rather than per product. In the next Section we will discuss the limitations of the previous PROFSET model. More details can be found elsewhere 3.2.1 LimitationsThe previous PROFSET model was specifically developed for market basket data from automated convenience st

19、ores. 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 pr

20、ofit 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 dat

21、a, 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 model to heavily underestimate the profit potential f

22、rom 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 man

23、age 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)

24、 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 ModelIn this section, we will highlight the improvements bein

25、g made to the previous PROFSET model 3.3.1 Profit AllocationAvoiding the equality constraint X = Tj results in different possible profit allocation systems. Indeed, it is important to recognize that the margin of transaction Tj can potentially be allocated to different frequent subsets of that trans

26、action. In other words, how should the margin m (Tj) be allocated to one or more different frequent subsets of Tj?The idea here is that we would like to know the purchase intentions of the customer who bought Tj . Unfortunately, since the customer has already left the store, we do not possess this i

27、nformation. However, if we can assume that some items occur more frequently together than others because they are considered complementary by customers, then frequent item sets may be interpreted as purchase intentions of customers. Consequently, there is the additional problem of finding out which

28、and how many purchase intentions are represented in a particular transaction Tj . Indeed, a transaction may contain several frequent subsets of different sizes, so it is not straightforward to determine which frequent sets represent the underlying purchase intentions of the customer at the time of s

29、hopping. Before proposing a solution to this problem, we will first define the concept of a maximal frequent subset of a transaction.Definition 1. Let F be the collection of all frequent subsets of a sales transaction Tj . Then is called maximal, denoted as X max , if and only if.: .Using this defin

30、ition, we will adopt the following rationale to allocate the margin m(Tj) of a sales transaction Tj .If there exists a frequent set X = Tj, then we allocate m(Tj) to M(X), just as in the previous PROFSET model. However, if there is no such frequent set, then one maximal frequent subset X will be dra

31、wn from all maximal frequent subsets according to the probability distribution, withAfter this, the margin m(X) is assigned to M(X) and the process is repeated for Tj X. In summary:Table 1 contains all frequent subsets of T for a particular transaction database. In this example, there is no unique m

32、aximal frequent subset of T. Indeed, there are two maximal frequent subsets of T, namely cola, peanuts and peanuts, cheese. Consequently, it is not obvious to which maximal frequent subset the profit margin m(T) should be allocated. Moreover, we would not allocate the entire profit margin m(T) to th

33、e selected item set, but rather the proportion m(X) that corresponds to the items contained in the selected maximal subset. Now how can one determine to which of both frequent subsets of T this margin should be allocated? As we have already discussed, the crucial idea here is that it really depends

34、on what has been the purchase intentions of the customer who purchased T. Unfortunately, one can never know exactly since we haven't asked the customer at the time of purchase. However, the support of the frequent subsets of T may provide some probabilistic estimation. Indeed, if the support of

35、a frequent subset is an indicator for the probability of occurrence of this purchase combination, then according to the data, customers buy the maximal subset cola, peanuts two times more frequently than the maximal subset peanuts, cheese. Consequently, we can say that it is more likely that the cus

36、tomer's purchase intention has been cola, peanuts instead of peanuts, cheese. This information is used to construct the probability distribution , reflecting the relative frequencies of the frequent subsets of T. Now, each time a sales transaction cola, peanuts, cheese is encountered in the data

37、, a random draw from the probability distribution will provide the most probable purchase intention (i.e. frequent subset) for that transaction. Consequently, on average in two of the three times this transaction is encountered, maximal subset cola, peanuts will be selected and m(cola; peanuts) will

38、 be allocated to M(cola; peanuts). After this, T is split up as follows: T := T cola; peanutsand the process of assigning the remaining margin is repeated as if the new T were a separate transaction, until T does not contain a frequent set anymore.3.2 Category Management RestrictionsAs pointed out i

39、n Section 2.1, a second limitation of the previous PROFSET model is its inability to include category management restrictions. This sometimes causes the model to exclude even all products from one or more categories because they do not contribute enough to the overall profitability of the optimal se

40、t. This often contradicts with the mission of retailers to offer customers a wide range of products, even if some of those categories or products are not profitable enough. Indeed, customers expect supermarkets to carry a wide variety of products and cutting away categories / departments would be ag

41、ainst the customers' expectations about the supermarket and would harm the store's image. Therefore, we want to offer the retailer the ability to include category restrictions into the generalized PROFSET model. This can be accomplished by adding an additional index k to the product variable

42、 to account for category membership, and by adding constraints on the category level. Several kinds of category restrictions can be introduced: which and how many categories should be included in the optimal set, or how many products from each category should be included. The relevance of these rest

43、rictions can be illustrated by the following common practices in retailing. First, when composing a promotion leaflet, there is only limited space to display products and therefore it is important to optimize the product composition in order to maximize cross-selling effects between products and avo

44、id product cannibalization. Moreover, according to the particular retail environment, the retailer will include or exclude specific products or product categories in the leaflet. For example, the supermarket in this study attempts to differentiate from the competition by the following image componen

45、ts: fresh, profitable and friendly. Therefore, the promotion leaflet of the retailer emphasizes product categories that support this image, such as fresh vegetables and meat, freshly-baked bread, ready-made meals, and others. Second, product category constraints may reflect shelf space allocations t

46、o products. For instance, large categories have more product facings than smaller categories. These kind of constraints can easily be included in the generalized PROFSET model as will be discussed hereafter.13外文翻譯中文翻譯對零售超市數(shù)據(jù)進行最優(yōu)產品選擇的數(shù)據(jù)挖掘框架：廣義PROFSET模型第一章 引言當今幾乎所有的中大型零售商擁有電子銷售交易系統(tǒng)，零售商認識到，競爭優(yōu)勢將不再僅僅取決于

47、使用這些系統(tǒng)管理目的的庫存或便利客戶退房。相反的，誰能夠在提取這些數(shù)據(jù)背后隱藏的、由數(shù)據(jù)庫生成的信息，并用它來優(yōu)化其營銷決策，就能獲得競爭優(yōu)勢。在此背景下，能夠最成功地從這些數(shù)據(jù)中提取可操作信息的零售商，他們提取的信息在零售行業(yè)中是至關重要的，而且具有特有的競爭優(yōu)勢。如果我們假設關聯(lián)規(guī)則挖掘具有一些熟悉的基本概念，從大型零售數(shù)據(jù)庫運用關聯(lián)規(guī)則挖掘2，可以幫助零售商成功地提取這方面的知識。近年來，隨著關聯(lián)規(guī)則利潤的發(fā)展，在零售市場分析方向的許多區(qū)域出現(xiàn)了投資現(xiàn)象。目前，基于此規(guī)則，已經發(fā)展了一些利潤客觀評價方法，以便排除一些無利潤因素；例如：規(guī)則數(shù)據(jù)特性的支持和密度、利潤、應用的完整性、J-規(guī)則

48、以及關聯(lián)。其他的方法是基于此規(guī)則的同步性發(fā)展起來的。 其次，人們已經意識到掌握這些信息，在決定這些規(guī)則的相關利潤時扮演極為重要的角色，然而，例如像不可預測性、行為能力和規(guī)則模板的利潤客觀標準已經被提出，最終，在零售商微觀經濟框架理論的協(xié)助下，當今主流的研究方向已經轉向關聯(lián)利潤的評估，更重要的是，它已經用于在大型企業(yè)的決策制定，以加強統(tǒng)一性。在本文的后部分，作者優(yōu)先介紹了一種面向產品選擇的模塊PROFSET。它在零售知識上，從質量管理和數(shù)量管理兩方面進行了闡述，為的是能夠對特定規(guī)格的產品產生最大的效益。這個模塊的關鍵點在于它不能基于個體特性來進行選擇，而是基于它們產生的特性集合，包括因交叉交易產

49、生的特性。但是最初它還不能克服在超級市場中表現(xiàn)出的一些缺陷，為解決之一問題，本文引入了一種現(xiàn)有PROFSET模塊的重要改進版，可以有效地運用到大型市場上。進一步，我們發(fā)展了一種專業(yè)于零售行業(yè)的模塊，包括產品種類管理規(guī)則，以便讓模塊色輸出更加真實。本文接下來的內容分布如下：第二章，我們介紹以前PROFSET模塊的局限性；第三章，介紹集成化PROFSE模塊；第四章，介紹集成化PROFSET模塊在實用市場數(shù)據(jù)方面的一些以有點；最后在第五章，總結本文，并介紹一些將來研究方向。第二章 PROFSET模塊 PROFSET模塊概念的關鍵之處在于當評價一個商品的商業(yè)價值時，不僅要看到它本身的個體效益（自然方法

50、），更要考慮在交易過程中與其他產品相結合時的效益。然而，當評價一種產品的市場效益時，必須從全局出發(fā)，而不是著眼于個體，因為前者更能反映市場上消費者多次、重復購買的市場特性。正如Cabena等人提出的觀點，疏忽了產品之間的聯(lián)系，就會失去了解市場上商業(yè)間相互聯(lián)系所產生的價值。而PROFSET模塊的設計目的正是用來解決這樣的問題。實際上，如果利用聯(lián)系的觀點看，一瓶酒加牡蠣的價值等同于一加侖的牛奶加谷物的價值，這個例子說明，當評價聯(lián)系商品的市場價值時，必須考慮進零售的圍觀市場因素。PROFSET通過評價在每次交易中不同產品產生的利潤差，進一步評價交叉銷售的可能性，而不是對單個商品進行評價。在下面的內容中，將講述到以前PROFSET模塊的局限性以及更多的細節(jié)。2.1局限性 以前的PROFSET模塊專門從自動便利超市對市場采購數(shù)據(jù)進行研究和發(fā)展，由于消費者一般不會在一次消費中購買大量的同一種商品，故這種原始的數(shù)據(jù)主要特點是針對小型市場采購。但是，在多次重復這種情況時，我們就可以通過把這些單個的利潤(Tj)相加較準確地計算出總的利潤差值(X)，例如X = Tj。不過，對于超級市場采購量會遠遠大于這些小型多次采購，這樣，問題就變得嚴重復雜；的確，在超級市場的采購不會多余7種產品，但每種產品的采購量可以達到10到15件，結果就是，PROFSET模塊無法在這樣復雜的交易過程下對綜合利潤差進行評價