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Actionable Knowledge Discovery approaches to extract the business and technical significant actions/patterns to support direct decision making. These actions suggest how to transform an object from an undesirable status to a desirable status by incurring less cost and high profit. This article aims to propose a work that generates actionable patterns efficiently. It reduces the search space and number of iterations for attribute value change during action generation. Performance of the proposed method is compared with Yang's method and OF-CEAMA on the basis of four parameters i.e. the total number of rules required for action generation, run time of the methods, the total number of generated actions, total net profit and time and space complexity. Experiments have been carried out on four datasets retrieved from UCI Machine learning repository. Experimental results show that the proposed work takes less time than the other two methods to extract actions for all datasets. Also, the number of rules required to generate actions are less than the other two methods. Results also suggest that a decrease in execution time does not compromise the information and proposed work generates the same actions and net profit. Moreover, the proposed work tries to transfer an object from undesired status to the desired status.

Recent renewed interest in computational writer identification has resulted in an increased number of publications. In relation to historical musicology its application has so far been limited. One of the obstacles seems to be that the clarity of the images from the scans available for computational analysis is often not sufficient. In this paper, the use of the Hinge feature is proposed to avoid segmentation and staff-line removal for effective feature extraction from low quality scans. The use of an auto encoder in Hinge feature space is suggested as an alternative to staff-line removal by image processing, and their performance is compared. The result of the experiment shows an accuracy of 87 % for the dataset containing 84 writers’ samples, and superiority of our segmentation and staff-line removal free approach. Practical analysis on Bach’s autograph manuscript of the Well-Tempered Clavier II (Additional MS. 35021 in the British Library, London) is also presented and the extensive applicability of our approach is demonstrated.

Real-world data mining generally must consider and involve domain and business oriented factors such as human knowledge, constraints and business expectations. This encourages the development of a domain driven methodology to strengthen data-centered pattern mining. This report presents a review of the ACM SIGKDD Workshop on Domain Driven Data Mining (DDDM2007), held in conjunction with the Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD07), which was held in San Jose, USA on 12 August, 2007. The aims and objectives of this workshop were to provide a premier forum for sharing innovative findings, knowledge, insights, experiences and lessons in tackling challenges met in domain driven, actionable knowledge discovery in the real world.

An increasing number of publications concerning data mining research in human resource management (HRM) gives an impression of a prospering new research field. The current paper reviews research on HR data mining to uncover systematically recent advancements as well as remaining tasks for future work. To provide a systematic analysis, an initial framework with central dimensions of domain-driven data mining research in HR is firstly elaborated and the method of identifying and reviewing contributions is depicted. The results of the review are presented and implications for future research on domain-driven data mining in HRM are derived.

[Excerpt] Following the success of SIGKDD-DDDM2007, ICDM-DDDM2008, ICDM-DDDM2009, and ICDMDDDM2010, the “2011 International Workshop on Domain Driven Data Mining (DDDM2011)” workshop aims to provide a premier forum for sharing findings, knowledge, insight, experience and lessons in tackling potential challenges in discovering actionable knowledge from complex domain problems, promoting interaction and filling the gap between academia and business, and driving a paradigm shift from interesting hidden pattern mining to actionable knowledge discovery in varying data mining domains. [...], (undefined)

On the basis of the discussions and retrospection on existing data mining methodologies and techniques in Chapter 1, this chapter presents an overall picture of domain driven data mining (D 3 M). We focus on the high level of architecture and concepts of the D 3 M methodology.

This chapter introduces the challenges and trends of data mining and knowledge discovery from data (KDD). We emphasize the issues surrounding real-world data mining, distinguish data-centered data mining from domain driven data mining, and propose the trends from data-centered hidden pattern discovery to domain driven actionable knowledge delivery(AKD) as a new KDD paradigm shift.

This chapter discusses a new technique – agent-driven data mining for domain driven data mining (D 3 M). We focus on introduce the basic concept, driving forces, technical means, research issues and case studies of agent-driven data mining.

The previous chapters have constructed key foundations for domain driven data mining. From this chapter, we start to discuss techniques, means and case studies for implementing domain driven actionable knowledge discoveryAKD and delivery.

Data Mining is an iterative, multi-step process consisting of different phases such as domain (or business) understanding, data understanding, data preparation, modeling, evaluation and deployment. Various data mining tasks are dependent on the human user for their execution. These tasks and activities that require human intelligence are not amenable to automation like tasks in other phases such as data preparation or modeling are. Nearly all Data Mining methodologies acknowledge the importance of the human user but do not clearly delineate and explain the tasks where human intelligence should be leveraged or in what manner. In this chapter we propose to describe various tasks of the domain understanding phase which require human intelligence for their appropriate execution.

Traditionally, data mining is an autonomous data-driven trial-and-error process. Its typical task is to let data tell a story disclosing hidden information, in which domain intelligence may not be necessary in targeting the demonstration of an algorithm. Often knowledge discovered is not generally interesting to business needs. Comparably, realworld applications rely on knowledge for taking effective actions. In retrospect of the evolution of KDD, this paper briefly introduces domain-driven data mining to complement traditional KDD. Domain intelligenceis highlighted towards actionable knowledge discovery, which involves aspects such as domain knowledge, people, environment and evaluation. We illustrate it through mining activity patterns in social security data. © World Scientific Publishing Company.

Traditionally, knowledge actionability has been investigated mainly by developing and improving technical interestingness. Recently, initial work on technical subjective interestingness and business-oriented profit mining presents general potential, while it is a long-term mission to bridge the gap between technical significance and business expectation. In this paper, we propose a two-way significance framework for measuring knowledge actionability, which highlights both technical interestingness and domain-specific expectations. We further develop a fuzzy interestingness aggregation mechanism to generate a ranked final pattern set balancing technical and business interests. Real-life data mining applications show the proposed knowledge actionability framework can complement technical interestingness while satisfy real user needs. © 2007, Inderscience Publishers.