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.
A reader is refereed to recent surveys on mining actionable knowledge @cite_15 @cite_29 , which discusses data mining for decision support (follow up actions). The role of domain experts is emphasized in producing action rules from data mining results. The implications of data mining to the actions have been under discussion for more than a decade @cite_27 ; however, these investigations typically focus on different aspects of knowledge discovery process than our study. In particular, actions are not learned directly from the data, but follow up actions as a result of discovering an interesting rule are considered. Our learning process is focused on learning action rules from data. Discovering action rules either requires prior extraction of classification rules or the rules are mined directly from data. The proposed Seek approach falls into the first type, while the Twist and the Contextual approaches represent the second type.
@cite_27 @cite_15 @cite_29
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The data mining process consists of a series of steps ranging from data cleaning, data selection and transformation, to pattern evaluation and visualization. One of the central problems in data mining is to make the mined patterns or knowledge actionable. Here, the term actionable refers to the mined patterns suggest concrete and profitable actions to the decision-maker. That is, the user can do something to bring direct benefits (increase in profits, reduction in cost, improvement in efficiency, etc.) to the organization's advantage. However, there has been written no comprehensive survey available on this topic. The goal of this paper is to fill the void. In this paper, we first present two frameworks for mining actionable knowledge that are inexplicitly adopted by existing research methods. Then we try to situate some of the research on this topic from two different viewpoints: 1) data mining tasks and 2) adopted framework. Finally, we specify issues that are either not addressed or insufficiently studied yet and conclude the paper. Actionable knowledge has been qualitatively and intensively studied in the social sciences. Its marriage with data mining is only a recent story. On the one hand, data mining has been booming for a while and has attracted an increasing variety of increasing applications. On the other, it is a reality that the so-called knowledge discovered from data by following the classic frameworks often cannot support meaningful decision-making actions. This shows the poor relationship and significant gap between data mining research and practice, and between knowledge, power, and action, and forms an increasing imbalance between research outcomes and business needs. Thorough and innovative retrospection and thinking are timely in bridging the gaps and promoting data mining toward next-generation research and development: namely, the paradigm shift from knowledge discovery from data to actionable knowledge discovery and delivery. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc. 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. 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.