A Profit-based Business Model for Evaluating Rule Interestingness

Transcription

1 A Profit-based Business Model for Evaluating Rule Interestingness Yaohua Chen, Yan Zhao and Yiyu Yao Department of Computer Science, University of Regina Regina, Saskatchewan, Canada S4S 0A2 Abstract. Different types of rules are mined from transaction databases often with the goal of improving sales and services. In this paper, we link the interestingness of rules with the context of business marketing. We consider the profits generated from some specific marketing strategies that are developed based on particular discovered rules. This leads to a profit-based business model for evaluating rule interestingness. With this additional utility, we investigate some relationships between different marketing strategies and fundamental properties of rules for profit increasing. 1 Introduction The rule mining was initiated as market basket analysis, which is used to discover cooccurrence or correlation relationships in a set of commercial items that are recorded in a large volumes of customer transaction database [1]. For example, an association rule can be expressed as for people who buy spaghetti, wine, and sauce (A), they also buy garlic bread (B), or symbolically, A B. Since rule mining is virtually practical, it has a direct impact on business and takes up many actionable concerns such as maximizing profit and payoff, minimizing cost, and finally leading to a wise action and decision making [2]. From the application view of rule mining, a rule is considered to be interesting if it is novel, potentially useful, understandable, actionable, profitable or explainable [4, 8]. Moreover, a rule is only advantaged while it can be understood and rationalized [19]. These lead to different philosophies in designing data mining solutions to real world problems and measures to evaluate rule interestingness [7]. Based on measurement and utility theories, a measure is proposed to evaluate one aspect of usefulness or interestingness of rules with respect to a particular context or user preference [5, 15, 18]. Different preferences of discovered rules must be represented quantitatively by different measures. It is difficult to justify and argue the usefulness or interestingness of a rule without concerning its usage or preference [17]. One needs to examine various circumstances in which rules are built and applied. Similar to the practice of medicine, the medicine treatment plans are determined based on the medical knowledge and practically clinical analysis and judgements [3]. In this paper, we link conventional rule mining with the concern of business profit. Typically, a discovered rule can be viewed as a type of knowledge or a belief about

2 purchasing behaviors or patterns of customers. Thus, rules can be used to design promotional packages or arrange cross-selling aiming to achieve more profit. For example, suppose a department store attempts to use an association rule, A B, to improve sales and services [1, 16]. Items in A and B may be located adjacent to each other in order to achieve an implicit recommendation and provide customers more convenience based on the learned rule A B. A simple way to evaluate rules is to apply the marketing strategies developed from rules to the real world markets, and then check whether higher profits can be generated. In fact, a profit-based model is necessary and essential to evaluate the interestingness of discovered rules. However, there exist some difficulties to implement such strategies in practice. Therefore, an alternative way is to build a model to analyze the discovered rules based on some basic business assumptions. This offers us a new interpretation and view of rules. In this paper, we introduce a profit-based business model and consider two types of marketing strategies to increase profits. The relationships among discovered rules, marketing strategies and profit increments are investigated. In other words, the statistical factors of rules are analyzed based on the criterion, such that the corresponding marketing strategies can generate high profits. The results show that different types of rules might be useful for different types of marketing strategies. The study proposed in this paper provides a basic and novel approach to evaluate the interestingness of rules in the context of business and economy. It is evident that an evaluation or measurement of rules should be linked to some particular contexts. This result may provide new opportunities and challenges on research and development of data mining systems. 2 Related Work Many economic utilities have been studied in order to identify cost-effective and profitbenefit association rules [12, 14, 16, 17]. Kleinberg et al. provided a microeconomic view of data mining [10]. They considered decision theory in the domain of data mining and argued that data mining is about extracting actionable rules which can increase utility, such as profit, security or loyalty. Wang et al. proposed a profit-based association rule mining model, called profit mining. They focused on a recommender that recommends items that maximize profit from future customers [16]. Lin et al. argued that people should consider the use of rules in the context of marketing [11]. An added value, such as profit, privacy, importance, uncertainty, or benefit of itemsets, is introduced into association rule mining model. Some measures in actionable rule mining deals with profit-driven actions required by business decision making [12 14]. A rule is referred to as actionable if the user can apply it to do something. An action may be some business strategies or promotions to change the non-desirable /non-profitable rules to desirable/profitable rules. For business users, actionable rule mining can help them to influence and control their changes or actions to obtain higher profits from consumers.

3 3 Rules and Probabilistic Interpretations In data mining, rules are typically interpreted in terms of probability. Different probabilistic measures can be defined to reflect various aspects of rules [20]. In this section, several probabilistic measures are investigated. The sales data of a supermarket or a company can be recorded as a binary table of transactions, called a transaction table. A transaction table can be formally defined by where S = (T, I, {V i i I}, {R i i I}), T is a finite nonempty set of transactions, I is a finite nonempty set of items, V i is a set of binary values {0, 1} for i I, R i : T V i is a function between transactions and items. Each function R i maps a transaction in T to a binary value of V i for an item i I. If a transaction t T contains an item i, then the value R i (t) is 1, otherwise, R i (t) is 0. In a transaction table, the rows correspond to transaction records, the column correspond to items, and each cell is a binary value of a transaction with respect to an item. A subset of items A I is called an itemset. Each transaction t contains an itemset. For an itemset A I, let m(a) denote the portion of database formed by transactions containing all items in A, namely, m(a) = {t T i A, R i (t) = 1}. The itemset A acts as a condition for the selection of transactions from the transaction database T. With this notation, we have T = m( ). For two disjoint itemsets, A, B I and A B =, an implication of the form A B is used to express a rule. It shows the relationship between purchasing itemset A and purchasing itemset B. A quantitative measure, called generality, of an itemset A I is defined by: G(A) = m(a) T = P (A), where denotes the cardinality of a set, and P denotes the probability in statistics. This measure indicates the relative size of the transactions containing the itemset A. The quantity may be viewed as the probability of a transaction t containing itemset A. Obviously, we have 0 G(A) 1. Moreover, the generality of the itemsets A and B is expressed by: G(A, B) = m(a B) T = m(a) m(b) T = P (A, B), which may be viewed as the joint probability of a transaction t containing itemsets A and B. The absolute support of an itemset B provided by A is defined by: AS(A, B) = m(a B) m(a) = P (A, B) P (A) = P (B A).

4 This quantity shows the degree to which A implies B, B depends on A or B associates with A. It may be viewed as the conditional probability of transactions containing the itemset B given that they contain the itemset A. The range of this measure is 0 AS(A, B) 1. The change support of an itemset B provided by A is defined by: CS(A, B) = AS(A, B) G(B) = P (B A) P (B). One may consider G(B) to be the prior probability of B and AS(A, B) the posterior probability of B after knowing A. The difference of posterior and prior probabilities represents the change of our confidence regarding whether A actually relates to B. The range of change support is from 1 to 1. For a positive value, one may say that A is positively related to B; for a negative value, one may say that A is negatively related to B. Three measures, absolute support AS(A, B), generality G(A, B) and change support CS(A, B), are used to define and quantify peculiarity rules and association rules [1, 20]. We can qualitatively characterize association and peculiarity rules in the following table: Rules AS(A, B) G(A, B) CS(A, B) Peculiarity Rules High Low High Association Rules High High Low Generality G(A, B) and absolute support AS(A, B) are also called the support and confidence in association rule mining [1]. 4 A Profit-based Business Model In this section, we propose a profit-based business model for evaluating mined rules. Profit is one of the primary financial objectives of a business [6]. It is one of basic tasks of business management. A general model of profit can be defined by: [6] P F (A) = [f(a) f (A) f (A) f (A)] V (A) F OE, (1) where P F (A) is the total amount of profit on the itemset A, f(a) is the sales price of the itemset A, f (A) represents the product costs of producing or purchasing the itemset A, f (A) represents unit-driven costs on the itemset A, such as the costs of shipping, handling and packaging, f (A) represents revenue-driven costs on the itemset A, such as commissions paid to salespersons and credit card discounts paid by retailers to the banks. These different costs are incremental (i.e. increased with each additional unit sold), V (A) is sales volume of A (i.e. total number of units of A actually are sold over a period), and F OE is fixed operating costs, such as rent, depreciation, salaries, and utilities. The amount of profit per unit after deducting various costs, including product costs, unit-driven costs and revenue-driven costs, from sales price is called unit margin or unit profit [6]. When the generality (probability) of selling itemset A, G(A), and

5 total number of transactions T are known, the sales volume can be considered as V (A) = m(a) = T G(A). Let p(a) denote the unit profit, that is, p(a) = f(a) f (A) + f (A) + f (A). Then, the Equation 1 can be re-expressed simply as P F (A) = p(a) V (A) F OE. (2) In this paper, the fixed operating costs F OE are assumed to be unaffected by a marketing strategy. The other two factors, unit profit p(a) and sales volume V (A), affect each other [9]. That is, when unit profit is increased, the sales volume is probably decreased. Conversely, when sales volume is increased, the unit profit probably has to be decreased. Their relationship can be demonstrated in Figure 1 [9]. Accordingly, mar- Unit Profit Sales Volume Fig. 1. Distribution between sales price and sales volume keting strategies to increase the profit can be generally classified into two groups based on the two factors in the profit model: Price-based strategy: to increase the unit profit (e.g. raising sales price, or reducing various costs). Volume-based strategy: to increase the sales volume (e.g. reducing sales price, or recommend and advertising items). A measure, called profit change, can be defined to quantify the difference of the profits generated after and before applying a particular marketing strategy. Let s denote a particular marketing strategy. The profit change on itemset A, denoted as CP s (A), with respect to the marketing strategy s is formally defined by CP s (A) = P F s (A) P F (A), = p s (A) V s (A) p(a) V (A), (3) where P F s (A), p s (A) and V s (A) represent the profit, unit profit and sales volume on A after applying the marketing strategy s, respectively, and P F (A), p(a) and V (A) represent the profit, unit profit and sales volume on A before applying the marketing strategy, respectively. Obviously, the profit change on itemsets A and B can be expressed as: CP s (A, B) = [P F s (A) P F (A)] + [P F s (B) P F (B)], = CP s (A) + CP s (B). (4)

6 The cost of running marketing strategy can be counted as various costs in the profit model. A positive profit change, CP s (A, B) > 0, means the profit increased, and a negative profit change, CP s (A, B) < 0, states the profit decreased after applying the marketing strategy s. 5 An Analysis on Marketing Strategies, Discovered Rules and Profit Changes Discovered rules can be used to develop the two types of marketing strategies for profit increasing. In this section, based on the profit-based business model, we investigate relationships between two types of rules and profit changes with respect to the pricebased and volume-based marketing strategies. 5.1 Profit Changes Based on Price Changes A price-based strategy is to raise the unit profits of items to increase the total profits, such as increasing sales price, reducing the unit costs, or cutting some services. With respect to a rule A B and A B, two types of price-based strategies are generally considered and analyzed as follows. Price-based strategy on itemset A: Let s 1 denote a price-based strategy on itemset A. This strategy probably leads to decrease the sales volume on the itemset A. The decreased sales volume and increased unit profit can be viewed as a kind of tradeoff. Thus, we suppose that the profit on the itemset A after applying the strategy s 1 is (at least) the same as the profit before applying the strategy, that is, CP s1 (A) = 0. Then, the profit change CP s1 (A, B) on itemsets A and B is CP s1 (A, B) = CP s1 (B) = p(b) [V s1 (B) V (B)]. Suppose the association relationship between itemsets A and B is not changed. In other words, the absolute support AS(A, B) is not changed. Then, the difference of sales volumes on the itemset B can be expressed as V s1 (B) V (B) = V s1 (A) AS(A, B) V (A) AS(A, B), = [V s1 (A) V (A)] AS(A, B). Let V s1 (B) denote the change of sales volume on itemset B, that is, V s1 (B) = V s1 (B) V (B), and V s1 (A) denote the change of sales volume on itemset A, that is, V s1 (A) = V s1 (A) V (A). Then, the above equation can be expressed as and the profit change CP s1 (A, B) is V s1 (B) = V s1 (A) AS(A, B). (5) CP s1 (A, B) = p(b) V s1 (A) AS(A, B). (6) Since the unit profit p(b) is a constant, the profit change CP s1 (A, B) is determined by two factors: sales volume change V s1 (A) and absolute support AS(A, B). Therefore,

7 sales volume change on itemset A determines the direction of profit change, while the absolute support AS(A, B) determines the degree of profit change. A high absolute support can make profit change significantly. Moreover, the sales volume V (B) can be expressed as the total number of transactions multiplying the generality of itemset B. Thus, the difference of sales volumes on itemset B can also be expressed as V s1 (B) = T s1 G s1 (B) T G(B). A large generality of itemset B, G(B), means a small space to decrease, while a low generality of itemset B means a large space to decrease. We consider two types of rules, peculiarity rules [20] and association rules [1], with high absolute supports used in this type of price-based strategies. The relationships between profit change and the two types of rules are generally summarized in Table 1. From Table 1, we can obtain a general idea such that the profit decreasing based on the Table 1. Relationships between profit changes and rules based on price increasing on A Discovered Probabilistic Interpretations Profit Change Rules AS(A, B) G(A, B) CS(A, B) G(A) G(B) CP s1 (A, B) Peculiarity Rules High Low High Low Low Low Association Rules High High Low High High Very Low association rules is more significant than the profit change based on the peculiarity rules when applying a price-based strategy on the itemset A. Example 1. Suppose a supermarket uses a peculiarity rule, printer ink, to develop a price-based strategy. The parameters of the rule are G(printer) = 2%, G(ink) = 1.6%, and G(printer, ink) = 1.6%, AS(printer, ink) = 80%, CS(printer, ink) = 78.4%. The unit profit and sales volume of printer are originally $50.00 and 200. The unit profit and sales volume of ink are originally $1.00 and 160. After applying the price-based strategy on printer, the unit profit of printer is raised to $60.00, however, the sales volume of printer is down to 150. Suppose the sales of ink depends on the sales of printer, so the sales volume of ink will go down to 120 as the sales volume of printer going down. Therefore, the total profit of printer and ink is changed from $10160 to $9120, decreased about 10%. Suppose a supermarket uses an association rule, milk eggs, to develop a pricebased strategy. The parameters of the rule are G(milk) = 80%, G(eggs) = 85%, and G(milk, eggs) = 75%, AS(milk, eggs) = 94%, CS(milk, eggs) = 9%. The unit profit and sales volume of milk are originally $1.00 and 8, 000. The unit profit and sales volume of eggs are originally $0.50 and 8, 500. After applying the price-based strategy on milk, the unit profit of milk is raised to $1.20, however, the sales volume of milk is down to 5, 000. Suppose the sales of eggs depends on the sales of milk, so the sales volume of eggs will go down to 5, 000 as the sales volume of milk going

8 down. Therefore, the total profit of milk and eggs is changed from $12250 to $8500, decreased about 31%. Price-based strategy on itemset B: Let s 2 denote a price-based strategy on itemset B. In this type of strategies, the unit profit and sales volume on the itemset A are supposed not to be changed. Thus, the profit change CP s2 (A, B) is CP s2 (A, B) = CP s2 (B) = p s2 (B) V s2 (B) p(b) V (B). If the rules show the larger dependence and larger generality of A and B, namely AS(A, B) and G(A, B) are very high, then increased unit profit on B may not make the sales volume decrease, otherwise, the unit profit increasing may lead to decrease the sales volume on itemset B. The relationships between profit change and two types of rules are generally summarized in Table 2. It shows the association rules can be useful to generate more profit Table 2. Relationships between profit change and rules based on price increasing on B Discovered Probabilistic Interpretations Profit Change Rules AS(A, B) G(A, B) CS(A, B) G(A) G(B) CP s2 (A, B) Peculiarity Rules High Low High Low Low Low Association Rules High High Low High High High than the peculiarity rules. Example 2. Suppose a supermarket uses a peculiarity rule, printer ink, to develop a price-based strategy. The parameters of the rule are G(printer) = 2%, G(ink) = 1.6%, and G(printer, ink) = 1.6%, AS(printer, ink) = 80%, CS(printer, ink) = 78.4%. The unit profit and sales volume of printer are originally $50.00 and 200. The unit profit and sales volume of ink are originally $1.00 and 160. After applying the price-based strategy on ink, the unit profit of ink is raised to $1.50. Suppose the sales of ink depends on the sales of printer, so the sales volume of ink will keep unchanged. Therefore, the total profit of printer and ink is changed from $10160 to $10240, increased about 0.7%. Suppose a supermarket uses an association rule, milk eggs, to develop a pricebased strategy. The parameters of the rule are G(milk) = 80%, G(eggs) = 85%, and G(milk, eggs) = 75%, AS(milk, eggs) = 94%, CS(milk, eggs) = 9%. The unit profit and sales volume of milk are originally $1.00 and 8, 000. The unit profit and sales volume of eggs are originally $0.50 and 8, 500. After applying the price-based strategy on eggs, the unit profit of eggs is raised to $1.00. Suppose the sales of eggs depends on the sales of milk, so the sales volume of eggs will keep unchanged. Therefore, the total profit of milk and eggs is changed from $12250 to $16500, increased about 35%. 5.2 Profit Changes Based on Volume Changes The volume-based strategies are to increase sales volumes (i.e. the amount of customers who buying items) to increase total profits. For example, one can reduce the sales price

9 or recommend items to customers to increase sales volumes. With respect to a rule, A B and A B, two types of volume-based strategies are generally considered: strategy on the itemset A and strategy on the itemset B. Volume-based strategies on itemset A: Let s 3 denote a volume-based strategy on itemset A. The unit profit on itemset A probably has to be reduced in order to increase the sales volume. The increased sales volume and decreased unit profit are viewed as a kind of tradeoff. Suppose the profit on itemset A after applying a volume-based strategy is the same as the profit before applying the volume-based strategy. That is, P F s (A) = P F (A) and CP s (A) = 0. Then, the profit change CP s3 (A, B) is CP s3 (A, B) = CP s3 (B) = p(b) [V s3 (B) V (B)], Suppose the association relationship between itemsets A and B is not changed, that is, the absolute support AS(A, B) is not changed. Then, the difference of sales volumes on the itemset B can be expressed as V s3 (B) V (B) = V s3 (A) AS(A, B) V (A) AS(A, B), = [V s3 (A) V (A)] AS(A, B). Let V s3 (B) denote the change of sales volume on itemset B, that is, V s3 (B) = V s3 (B) V (B), and V s3 (A) denote the change of sales volume on itemset A, that is, V s3 (A) = V s3 (A) V (A). Then, the above equation can be expressed as and the profit change CP s3 (A, B) is V s3 (B) = V s3 (A) AS(A, B), (7) CP s3 (A, B) = p(b) V s3 (A) AS(A, B). (8) Since the unit profit p(b) is a constant, the profit change CP s3 (A, B) is determined by two factors: sales volume change V s3 (A) and absolute support AS(A, B). Therefore, the sales volume on A determines the direction of the profit change, and the absolute support AS(A, B) determines the degree of profit decreasing. A high absolute support can make profit change significantly. Moreover, the sales volume V (B) can be expressed as the total number of transactions multiplying the probability of selling the itemset B. Thus, the difference of sales volumes on the itemset B can also be expressed as V s1 (B) = T s1 G s1 (B) T G(B). A large generality of B, G(B), means a small space to decrease, while a low generality of B means a large space to decrease. We consider two types of rules with high absolute supports used in this type of volume-based strategies. The relationships between profit change and the two types of rules are generally summarized in Table 3. Table 3 shows that the peculiarity rules can increase the profit more significantly than the association rules.

10 Table 3. Relationships between profit changes and Rules based on volume increasing on A Discovered Probabilistic Interpretations Profit Change Rules AS(A, B) G(A, B) CS(A, B) G(A) G(B) CP s3 (A, B) Peculiarity Rules High Low High Low Low High Association Rules High High Low High High Low Example 3. Suppose a supermarket uses a peculiarity rule, printer ink, to develop a volume-based strategy. The parameters of G(printer) = 2%, G(ink) = 1.6%, and G(printer, ink) = 1.6%, AS(printer, ink) = 80%, CS(printer, ink) = 78.4%. The unit profit and sales volume of printer are originally $50.00 and 200. The unit profit and sales volume of ink are originally $1.00 and 160. After applying the volumebased strategy on printer, the unit profit of printer is reduced to $5.00, and the sales volume of printer is up to Suppose the sales of ink depends on the sales of printer, so the sales volume of ink will go up to 1600 as the sales volume of printer going up. Therefore, the total profit of printer and ink is changed from $10160 to $11600, increased about 14%. Suppose a supermarket uses an association rule, milk eggs, to develop a volumebased strategy. The parameters of the rule are G(milk) = 80%, G(eggs) = 85%, and G(milk, eggs) = 75%, AS(milk, eggs) = 94%, CS(milk, eggs) = 9%. The unit profit and sales volume of milk are originally $1.00 and 8, 000. The unit profit and sales volume of eggs are originally $0.50 and 8, 500. After applying the volume-based strategy on milk, the unit profit of milk is reduced to $0.90, and the sales volume of milk is up to 8, 900. Suppose the sales of eggs depends on the sales of milk, so the sales volume of eggs will go up to 9, 000. Therefore, the total profit of milk and eggs is changed from $12250 to $12510, increased about 2%. Volume strategy on itemset B: Let s 4 denote a volume-based strategy on itemset B. The unit profit and sales volume on the itemset A are supposed not to be changed. Thus, the profit change CP s4 (A, B) is CP s4 (A, B) = CP s4 (B) = p s4 (B) V s4 (B) p(b) V (B). If the discovered rules show the larger dependence and larger generality of A and B, namely AS(A, B) and G(A, B) are very high, then the decreased unit profit on itemset B may not make the sales volume increase significantly, otherwise, the unit profit decreasing may lead to increase the sales volume significantly. That means stronger the association is between two itemsets, the more profit is decreased when applying a volume-based strategy on itemset B. Therefore, with a volume-based strategy, the relationships between profit changes and two types of rules are summarized in Table 4. Table 4 shows that by applying a volume-based strategy on itemset B, the association rules decrease the profit more significantly than the peculiarity rules. Example 4. Suppose a supermarket uses a peculiarity rule, printer ink, to develop a volume-based strategy. The parameters of the rule are G(printer) = 2%, G(ink) =

11 Table 4. Relationships between profit changes and Rules based on volume increasing on B Discovered Probabilistic Interpretations Profit Change Rules AS(A, B) G(A, B) CS(A, B) G(A) G(B) CP s4 (A, B) Peculiarity Rules High Low High Low Low Low Association Rules High High Low High High Very Low 1.6%, and G(printer, ink) = 1.6%, AS(printer, ink) = 80%, CS(printer, ink) = 78.4%. The unit profit and sales volume of printer are originally $50.00 and 200. The unit profit and sales volume of ink are originally $1.00 and 160. After applying the volume-based strategy on ink, the unit profit of ink is down to $0.90. Suppose the sales of ink depends on the sales of printer, so the sales volume of ink will keep unchanged. Therefore, the total profit of printer and ink is changed from $10160 to $10144, decreased about 0.2%. Suppose a supermarket uses an association rule, milk eggs, to develop a volumebased strategy. The parameters of the rule are G(milk) = 80%, G(eggs) = 85%, and G(milk, eggs) = 75%, AS(milk, eggs) = 94%, CS(milk, eggs) = 9%. The unit profit and sales volume of milk are originally $1.00 and 8, 000. The unit profit and sales volume of eggs are originally $0.50 and 8, 500. After applying the volume-based strategy on eggs, the unit profit of eggs is down to $0.40. Suppose the sales of eggs depends on the sales of milk, so the sales volume of eggs will keep unchanged. Therefore, the total profit of milk and eggs is changed from $12250 to $11400, decreased about 7%. Generally, based on the analysis above, we can summarize that different types of rules are useful for different marketing strategies. Based on the profit-based model, the association rules are more useful to develop price-based strategies, while the peculiarity rules are more useful to develop volume-based strategies. 6 Conclusion The common practice in data mining focuses more on algorithmic and statistical aspects of rules. Although many useful results have been achieved, the real application of discovered rules remains to be a challenging problem. Without a domain specific semantic interpretation of rules, one may not solve this problem successfully. In this paper, we introduce a profit-based business model for the semantics study and application. Two types of rules and two types of marketing strategies are considered. We analyze the marketing strategies where the rules are used to generate high profits. One can find out that requirements of rules to generate high profits in different marketing strategies are different. It confirms that semantics and domain knowledge should be incorporated into data mining process.

12 References 1. Agrawal, R., Imielinski, T., and Swami, A. Mining association rules between sets of items in large databases, Proceedings of ACM SIGMOD, , Braha, D. A theory of actionable data mining with application to semiconductor manufacturing control, International Journal of Production Research, to appear. 3. Coulehan, J.L. and Block, M.R. The Medical Interview: Mastering Skills for Clinical Practice, 5th Edition, F.A. Davis Co., Philadelphia, Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., and Uthurusamy, R. (Eds.), Advances in Knowledge Discovery and Data Mining, AAAI/MIT Press, California, Fishburn, P.C. Utility Theory for Decision Making, John Wiley & Sons, Inc., New York, Gardner, F.V. Profit Management and Control, McGraw-Hill Book Company, Inc., New York, Geng, L. and Hamilton, H. Interestingness measures for data mining: A survey, ACM Computing Surveys, 38, Article No. 9, Han, J. and Kamber, M. Data mining: Concept and Techniques, Morgan Kaufmann, Palo Alto, CA, Jobber, D. Principles and Practice of Marketing, Fouth edition, McGraw-Hill Europe, London, Kleinberg, J., Papadimitriou, C. H. and Raghavan, P. A microeconomic view of data mining, Data Mining and Knowledge Discovery, 2, 311C324, Lin, T.Y., Yao, Y.Y. and Louie, E. Mining value added association rules, Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD 02), , Ling, C., Chen, T., Yang, Q. and Chen, J. Mining optimal actions for profitable CRM, Proceedings of ICDM, , Liu, B., Hsu, W. and Ma, Y. Identifying non-actionable association rules, Proceedings of KDD, , Ras, Z. and Gupta, S. Global action rules in distributed knowledge systems, in: L. Czaja, H.D. Burkhard, and P. Starke (Eds.), Journal of Fundamenta Informaticae, 51, , Roberts, F. Measurement Theory, Addison Wesley, Massachusetts, Wang, K., Zhou, S. and Han, J. Profit mining: from patterns to actions, Proceedings of the 8th International Conference on Extending Database Technology (EDBT 02), 70-87, Weiss, G., Saar-Tsechansky, M. and Zadrozny, B. Report on UBDM-05: workshop on utilitybased data mining. SIGKDD Explorations, 7, , Yao, Y.Y., Chen, Y.H. and Yang, X.D. A Measurement-theoretic foundation for rule interestingness evaluation, In: Foundations and Novel Approaches in Data Mining Series: Studies in Computational Intelligence, 9, Lin, T.Y.; Ohsuga, S.; Liau, C.-J.; Hu, X. (Eds.), 41-59, Yao, Y.Y., Zhao, Y. and Maguire, R.B. Explanation-oriented association mining using a combination of unsupervised and supervised learning algorithms, Proceedings of the 16th Conference of the Canadian Society for Computational Studies of Intelligence (AI 03), , Zhong, N. Yao, Y.Y. and Ohshima, M. Peculiarity oriented multidatabase mining, IEEE Transactions on Knowledge and Data Engineering, 15, , 2003.