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Customer transactions tend to change overtime with changing customer behaviour patterns. Classifier models, however, are often designed to perform prediction on data which is assumed to be static. These classifier models thus deteriorate in performance overtime when predicting in the context of evolving data. Robust adaptive classification models are therefore needed to detect and adjust to the kind...
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...
Emerging patterns (EPs) are those item sets whose supports change significantly from one class to another. Studies have shown that they have very powerful distinguishing features and are very useful for constructing accurate classifiers. The task of finding such patterns is a challenging problem and efficient techniques for their mining are needed. Precious EP mining approaches often produce a large...
Computerization of hospital information enables us to visualize and analyze temporal characteristics of hospital services, which can be viewed as a first step to improve and innovate clinical services. % This paper proposes a temporal data mining process which consists of decision tree, clustering, MDS and three-dimensional trajectories mining. The results show that the reuse of stored data will give...
Privacy protection is one of the key requirements of smart grids. To understand the importance of privacy threats it is necessary to study nature of power signals. In this paper, we propose a well-known statistical method which relies on the empirical probability distribution. The method is used to reveal trends in the power signal data and how these trends are changed if a) different data sampling...
Natural events like climate, disease, etc., and man-made events like theft have a great impact in the regions where they occur. Hence, there is a need to assess the behavior of these events -- regions where they occur, the patterns they exhibit etc., to help manage them suitably. In addition, events that are dynamic in nature make it even more difficult to extract or understand such behavior. Our...
In this paper we propose a constraint programming approach for enumerating all frequent patterns with wildcards in a given sequence. To reduce the search space, we show that the anti-monotonicity property of frequent patterns can be dynamically encoded using no good recording based approach. Finally, the constraints network is encoded as a Boolean formula. This last step allows us to exploit the efficiency...
Many algorithms have been developed to identify important nodes in a complex network, including various centrality metrics and Page Rank, but most fail to consider the dynamic nature of the network. They therefore suffer from recency bias and fail to recognize important new nodes that have not had as much time to accumulate links as their older counterparts. This paper describes the Effective Contagion...
Convergence of data mining and process management is ideal -- but still limited. An example of such a convergence is presented in the form of Competency-driven Dynamic Resource Management (CDRM) Methodology that addresses the lack of agent-assignment-strategy in Workflow Management Systems (WfMS). Currently, WfMSs do not have any strategy to allocate resources (employees, agents) to their business...
Co-location pattern mining, which discovers feature types that frequently appear together in a nearby geographic region, is an important branch of spatial data mining. With the evolving of computation and communication technology, spatial information is included into more and more datasets. However, existing techniques of mining co-location patterns have to generate all candidate patterns for further...
The social network methodology has gained considerable attention recently. The main motivation is to construct and analyze social networks that involve actors from a specific application domain. The advanced computing technology has facilitated automating the process and provided flexibility, robustness and scalability. A large number of automated tools exist. Each tool supports specific functions...
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