Different machine learning techniques have been proposed and used for modeling individual and group user needs, interests and preferences. In the traditional predictive modeling instances are described by observable variables, called attributes. The goal is to learn a model for predicting the target variable for unseen instances. For example, for marketing purposes a company consider profiling a new user based on her observed web browsing behavior, referral keywords or other relevant information. In many real world applications the values of some attributes are not only observable, but can be actively decided by a decision maker. Furthermore, in some of such applications the decision maker is interested not only to generate accurate predictions, but to maximize the probability of the desired outcome. For example, a direct marketing manager can choose which type of a special offer to send to a client (actionable attribute), hoping that the right choice will result in a positive response with a higher probability. We study how to learn to choose the value of an actionable attribute in order to maximize the probability of a desired outcome in predictive modeling. We emphasize that not all instances are equally sensitive to changes in actions. Accurate choice of an action is critical for those instances, which are on the borderline (e.g. users who do not have a strong opinion one way or the other). We formulate three supervised learning approaches for learning to select the value of an actionable attribute at an instance level. We also introduce a focused training procedure which puts more emphasis on the situations where varying the action is the most likely to take the effect. The proof of concept experimental validation on two real-world case studies in web analytics and e-learning domains highlights the potential of the proposed approaches.
This paper proposes an algorithm to discover novel association rules, combined association rules. Compared with conventional association rule, this combined association rule allows users to perform actions directly. Combined association rules are always organized as rule sets, each of which is composed of a number of single combined association rules. These single rules consist of non-actionable attributes, actionable attributes, and class attribute, with the rules in one set sharing the same non-actionable attributes. Thus, for a group of objects having the same non-actionable attributes, the actions corresponding to a preferred class can be performed directly. However, standard association rule mining algorithms encounter many difficulties when applied to combined association rule mining, and hence new algorithms have to be developed for combined association rule mining. In this paper, we will focus on rule generation and interestingness measures in combined association rule mining. In rule generation, the frequent itemsets are discovered among itemset groups to improve efficiency. New interestingness measures are defined to discover more actionable knowledge. In the case study, the proposed algorithm is applied into the field of social security. The combined association rule provides much greater actionable knowledge to business owners and users. 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. Many applications can benefit from constructing models to predict the behavior of an entity. However, such models do not provide the user with explicit knowledge that can be directly used to influence (restrain or encourage) behavior for the user's interest. Undoubtedly, the user often exactly needs such knowledge. This type of knowledge is called actionable knowledge. Actionability is a very important criterion measuring the interestingness of mined patterns. In this paper, to mine such knowledge, we take a first step toward formally defining a new class of data mining problem, named actionable behavioral rule mining. Our definition explicitly states the problem as a search problem in a framework of support and expected utility. We also propose two algorithms for mining such rules. Our experiment shows the validity of our approach, as well as the practical value of our defined problem. Most data mining algorithms and tools stop at the mining and delivery of patterns satisfying expected technical interestingness. There are often many patterns mined but business people either are not interested in them or do not know what follow-up actions to take to support their business decisions. This issue has seriously affected the widespread employment of advanced data mining techniques in greatly promoting enterprise operational quality and productivity. In this paper, we present a formal view of actionable knowledge discovery (AKD) from the system and decision-making perspectives. AKD is a closed optimization problem-solving process from problem definition, framework model design to actionable pattern discovery, and is designed to deliver operable business rules that can be seamlessly associated or integrated with business processes and systems. To support such processes, we correspondingly propose, formalize, and illustrate four types of generic AKD frameworks: Postanalysis-based AKD, Unified-Interestingness-based AKD, Combined-Mining-based AKD, and Multisource Combined-Mining-based AKD (MSCM-AKD). A real-life case study of MSCM-based AKD is demonstrated to extract debt prevention patterns from social security data. Substantial experiments show that the proposed frameworks are sufficiently general, flexible, and practical to tackle many complex problems and applications by extracting actionable deliverables for instant decision making.