Dynamic Data Mining: Methodology and Algorithms

Supervised data stream mining has become an important and challenging data mining task in modern organizations. The key challenges are threefold: (1) a possibly infinite number of streaming examples and time-critical analysis constraints; (2) concept drift; and (3) skewed data distributions. To address these three challenges, this thesis proposes the novel dynamic data mining (DDM) methodology by effectively applying supervised ensemble models to data stream mining. DDM can be loosely defined as categorization-organization-selection of supervised ensemble models. It is inspired by the idea that although the underlying concepts in a data stream are time-varying, their distinctions can be identified. Therefore, the models trained on the distinct concepts can be dynamically selected in order to classify incoming examples of similar concepts. First, following the general paradigm of DDM, we examine the different concept-drifting stream mining scenarios and propose corresponding effective and efficient data mining algorithms. • To address concept drift caused merely by changes of variable distributions, which we term pseudo concept drift, base models built on categorized streaming data are organized and selected in line with their corresponding variable distribution characteristics. • To address concept drift caused by changes of variable and class joint distributions, which we term true concept drift, an effective data categorization scheme is introduced. A group of working models is dynamically organized and selected for reacting to the drifting concept. Secondly, we introduce an integration stream mining framework, enabling the paradigm advocated by DDM to be widely applicable for other stream mining problems. Therefore, we are able to introduce easily six effective algorithms for mining data streams with skewed class distributions. In addition, we also introduce a new ensemble model approach for batch learning, following the same methodology. Both theoretical and empirical studies demonstrate its effectiveness. Future work would be targeted at improving the effectiveness and efficiency of the proposed algorithms. Meantime, we would explore the possibilities of using the integration framework to solve other open stream mining research problems

Efficient Turbulent Compressible Convection in the Deep Stellar Atmosphere

This paper reports an application of gas-kinetic BGK scheme to the computation of turbulent compressible convection in the stellar interior. After incorporating the Sub-grid Scale (SGS) turbulence model into the BGK scheme, we tested the effects of numerical parameters on the quantitative relationships among the thermodynamic variables, their fluctuations and correlations in a very deep, initially gravity-stratified stellar atmosphere. Comparison indicates that the thermal properties and dynamic properties are dominated by different aspects of numerical models separately. An adjustable Deardorff constant in the SGS model $c_\mu=0.25$ and an amplitude of artificial viscosity in the gas-kinetic BGK scheme $C_2=0$ are appropriate for current study. We also calculated the density-weighted auto- and cross-correlation functions in Xiong's (\cite{xiong77}) turbulent stellar convection theories based on which the gradient type of models of the non-local transport and the anisotropy of the turbulence are preliminarily studied. No universal relations or constant parameters were found for these models.Comment: 13 pages, 8 figures, accepted by ChJA

Turbulent convection model in the overshooting region: II. Theoretical analysis

Turbulent convection models are thought to be good tools to deal with the convective overshooting in the stellar interior. However, they are too complex to be applied in calculations of stellar structure and evolution. In order to understand the physical processes of the convective overshooting and to simplify the application of turbulent convection models, a semi-analytic solution is necessary. We obtain the approximate solution and asymptotic solution of the turbulent convection model in the overshooting region, and find some important properties of the convective overshooting: I. The overshooting region can be partitioned into three parts: a thin region just outside the convective boundary with high efficiency of turbulent heat transfer, a power law dissipation region of turbulent kinetic energy in the middle, and a thermal dissipation area with rapidly decreasing turbulent kinetic energy. The decaying indices of the turbulent correlations $k$, $\bar{u_{r}'T'}$, and $\bar{T'T'}$ are only determined by the parameters of the TCM, and there is an equilibrium value of the anisotropic degree $\omega$. II. The overshooting length of the turbulent heat flux $\bar{u_{r}'T'}$ is about $1H_k$($H_k=|\frac{dr}{dlnk}|$). III. The value of the turbulent kinetic energy at the convective boundary $k_C$ can be estimated by a method called \textsl{the maximum of diffusion}. Turbulent correlations in the overshooting region can be estimated by using $k_C$ and exponentially decreasing functions with the decaying indices.Comment: 32 pages, 9 figures, Accepted by The Astrophysical Journa

Critical Success Factors for Effective Knowledge Sharing in Chinese Joint Ventures

Effective knowledge sharing is vital to the success of international joint ventures. To ensure that organizational knowledge in a joint venture can be smoothly communicated and exchanged between its employees in a multi-culture environment, the impact of culture on knowledge sharing needs to be well understood. This paper investigates the impact of culture on knowledge sharing in Chinese joint ventures. Using a multi-case study approach, this paper shows that effective communication, shared mindsets, training and leadership are the critical success factors for effective knowledge sharing in Chinese joint ventures. Such findings facilitate developing specific organizational culture that supports knowledge sharing and can lead to better organizational performance in the increasingly globalized economy