Most approaches to mining association rules implicitly consider the utilities of the itemsets to be equal. We assume that the utilities of itemsets may differ, and identify the high utility itemsets based on information in the transaction database and external information about utilities. Our theoretical analysis of the resulting problem lays the foundation for future utility mining algorithms. 1

An important issue of data mining is how to transfer data into information, the information into action, and the action into value or profit. This paper presents a study on applying sensitivity analysis to neural network models for a particular area in data mining, interesting mining and profit mining. Applying sensitivity analysis to neural network models rather than just regression models can help us identify sensible factors that play important roles to dependent variables such as total profit in a dynamic environment.

Summary. Many measures have been proposed and studied extensively in data mining for evaluating the interestingness (or usefulness) of discovered rules. They are usually defined based on structural characteristics or statistical information about the rules. The meaningfulness of each measure was interpreted based either on intuitive arguments or mathematical properties. There does not exist a framework in which one is able to represent the user judgment explicitly, precisely, and formally. Since the usefulness of discovered rules must be eventually judged by users, a framework that takes user preference or judgement into consideration will be very valuable. The objective of this paper is to propose such a framework based on the notion of user preference. The results are useful in establishing a measurementtheoretic foundation of rule interestingness evaluation.

The necessity to develop methods for discovering association patterns to increase business utility of an enterprise has long been recognized in data mining community. This requires modeling specific association patterns that are both statistically (based on support and confidence) and semantically (based on objective utility) relating to a given objective that a user wants to achieve or is interested in. However, we notice that no such a general model has been reported in the literature. Traditional association mining focuses on deriving correlations among a set of items and their association rules like ¢¤£¦¥¨§�©¨������©�©� � only tell us that a pattern like ��¢¤£¦¥¨§�©¨�� � is statistically related to an item like ��©�©� �. In this paper, we present a new approach, called Objective-Oriented utility-based Association (OOA) mining, to modeling such association patterns that are explicitly relating to a user’s objective and its utility. Due to its focus on a user’s objective and the use of objective utility as key semantic information to measure the usefulness of association patterns, OOA mining differs significantly from existing approaches such as the existing constraint-based association mining. We formally define OOA mining and develop an algorithm for mining OOA rules. The algorithm is an enhancement to Apriori with specific mechanisms for handling objective utility. We prove that the utility constraint is neither monotone nor anti-monotone nor succinct nor convertible and present a novel pruning strategy based on the utility constraint to improve the efficiency of OOA mining. 1

Data mining promises to discover valid and potentially useful patterns in data. Often, discovered patterns are not useful to the user. ”Actionability ” addresses this problem in that a pattern is deemed actionable if the user can act upon it in her favor. We introduce the notion of “action ” as a domain-independent way to model the domain knowledge. Given a data set about actionable features and an utility measure, a pattern is actionable if it summarizes a population that can be acted upon towards a more promising population observed with a higher utility. We present several pruning strategies taking into account the actionability requirement to reduce the search space, and algorithms for mining all actionable patterns as well as mining the top k actionable patterns. We evaluate the usefulness of patterns and the focus of search on a real-world application domain.

Game theory is a powerful tool for the analysis of the competitions among manufacturers in a market. In this paper, we present a study on combining game theory and data mining by introducing the concept of domination game analysis. We present a multidimensional market model, where every dimension represents one attribute of a commodity. Every product or customer is represented by a point in the multidimensional space, and a product is said to “dominate ” a customer if all of its attributes can satisfy the requirements of the customer. The expected market share of a product is measured by the expected number of the buyers in the customers, all of which are equally likely to buy any product dominating him. A Nash Equilibrium is a configuration of the products achieving stable expected market shares for all products. We prove that Nash Equilibrium in such a model can be computed in polynomial time if every manufacturer tries to modify its product in a round robin manner. To further improve the efficiency of the computation, we also design two algorithms for the manufacturers to efficiently find their best response to other products in the market.

Abstract. This paper presents a new philosophical view and methodology for data mining. A framework of explanation oriented data mining is proposed and studied with respect to association mining. The notion of conditional associations is adopted, which explicitly expresses the conditions under which an association occurs. To illustrate the basic ideas, the theory of rough sets is used to construct explanations. 1

Action rules provide hints to a business user what actions (i.e., changes within some values of flexible attributes) should be taken to improve the profitability of customers. That is, taking some actions to re-classify some customers from less desired decision class to the more desired one. However, in previous work, each action rule was constructed from two rules, extracted earlier, defining different profitability classes. In this paper, we make a first step towards formally introducing the problem of mining action rules from scratch and present formal definitions. In contrast to previous work, our formulation provides guarantee on verifying completeness and correctness of discovered action rules. In addition to formulating the problem from an inductive learning viewpoint, we provide theoretical analysis on the complexities of the problem and its variations. Furthermore, we present efficient algorithms for mining action rules from scratch. In an experimental study we demonstrate the usefulness of our techniques.

This paper addresses some fundamental issues related to the foundations of data mining. It is argued that there is an urgent need for formal and mathematical modeling of data mining. A formal framework provides a solid basis for a systematic study of many fundamental issues, such as representations and interpretations of primitive notions of data mining, data mining algorithms, explanations and applications of data mining results. A multi-level framework is proposed for modeling data mining based on results from many related fields. Formal concepts are adopted as the primitive notion. A concept is jointly defined as a pair consisting of the intension and the extension of the concept, namely, a formula in a certain language and a subset of the universe. An object satisfies the formula of a concept if the object has the properties as specified by the formula, and the object belongs to the extension of the concept. Rules are used to describe relationships between concepts. A rule is expressed in terms of the intensions of the two concepts and is interpreted in terms of the extensions of the concepts. Several different types of rules are investigated. The usefulness and meaningfulness of discovered knowledge are examined using a utility model and an explanation model.

While many data mining models concentrate on automation and efficiency, interactive data mining models focus on adaptive and effective communications between human users and computer systems. User views, preferences, strategies and judgements play the most important roles in human-machine interactivities, guide the selection of target knowledge representations, operations, and measurements. Practically, user views, preferences and judgements also decide strategies of abnormal situation handling, and explanations of mined patterns. In this paper, we discuss these fundamental issues. 1.