Special issue on advances in data mining and its applications

Data mining is the process to identify valid, implicit, previously unknown, potentially useful and understandable information from large databases. It is an important step in the process of knowledge discovery in databases, (Olaru & Wehenkel, 1999). In a data mining process, input data can be structured, seme-structured, or unstructured. Data can be in text, categorical or numerical values. One of the important characteristics of data mining is its ability to deal data with large volume, distributed, time variant, noisy, and high dimensionality. A large number of data mining algorithms have been developed for different applications. For example, association rules mining can be useful for market basket problems, clustering algorithms can be used to discover trends in unsupervised learning problems, classification algorithms can be applied in decision-making problems, and sequential and time series mining algorithms can be used in predicting events, fault detection, and other supervised learning problems (Vapnik, 1999). Classification is among the most important tasks in the data mining, particularly for data mining applications into engineering fields. Together with regression, classification is mainly for predictive modelling. So far, there have been a number of classification algorithms in practice. According to (Sebastiani, 2002), the main classification algorithms can be categorized as: decision tree and rule based approach such as C4.5 (Quinlan, 1996); probability methods such as Bayesian classifier (Lewis, 1998); on-line methods such as Winnow (Littlestone, 1988) and CVFDT (Hulten 2001), neural networks methods (Rumelhart, Hinton & Wiliams, 1986); example-based methods such as k-nearest neighbors (Duda & Hart, 1973), and SVM (Cortes & Vapnik, 1995). Other important techniques for classification tasks include Associative Classification (Liu et al, 1998) and Ensemble Classification (Tumer, 1996).

Development and evaluation of neural network freeway incident detection models using field data

This paper discusses a multi-layer feedforward (MLF) neural network incident detection model that was developed and evaluated using field data. In contrast to published neural network incident detection models which relied on simulated or limited field data for model development and testing, the model described in this paper was trained and tested on a real-world data set of 100 incidents. The model uses speed, flow and occupancy data measured at dual stations, averaged across all lanes and only from time interval t. The off-line performance of the model is reported under both incident and non-incident conditions. The incident detection performance of the model is reported based on a validation-test data set of 40 incidents that were independent of the 60 incidents used for training. The false alarm rates of the model are evaluated based on non-incident data that were collected from a freeway section which was video-taped for a period of 33 days. A comparative evaluation between the neural network model and the incident detection model in operation on Melbourne's freeways is also presented. The results of the comparative performance evaluation clearly demonstrate the substantial improvement in incident detection performance obtained by the neural network model. The paper also presents additional results that demonstrate how improvements in model performance can be achieved using variable decision thresholds. Finally, the model's fault-tolerance under conditions of corrupt or missing data is investigated and the impact of loop detector failure/malfunction on the performance of the trained model is evaluated and discussed. The results presented in this paper provide a comprehensive evaluation of the developed model and confirm that neural network models can provide fast and reliable incident detection on freeways. (C) 1997 Elsevier Science Ltd. All rights reserved.

Causes of communication problems at work: A qualitative analysis of supervisor-subordinate differences

This study provided information about how individual workers perceive, describe and interpret episodes of problematic communication. Sixteen full-time workers (5 males, 11 females) were interviewed in depth about specific incidents of problematic communication within their workplace. Their descriptions of the attributed causes of the incidents were coded using a categorisation scheme developed from Coupland, Wieman, and Giles' (1991) model of sources of problematic communication. Communication problems were most commonly attributed to individual deficiency and group membership, although there were differences depending on the direction of communication. The most negative attributions (to personality flaws, to lack of skills, and to negative stereotypes of the outgroup) were most commonly applied by individuals to their supervisors, whilst attributions applied to co-workers and subordinates tended to be less negative, or even positive in some instances (where individuals attributed the fault to themselves). Overall, results highlighted distinctions between the perceptions of communication problems with supervisors and with subordinates, and are interpreted with reference to social identity theory.

Effect of material anisotropy on the onset of convective flow in three-dimensional fluid-saturated faults

In order to investigate the effect of material anisotropy on convective instability of three-dimensional fluid-saturated faults, an exact analytical solution for the critical Rayleigh number of three-dimensional convective flow has been obtained. Using this critical Rayleigh number, effects of different permeability ratios and thermal conductivity ratios on convective instability of a vertically oriented three-dimensional fault have been examined in detail. It has been recognized that (1) if the fault material is isotropic in the horizontal direction, the horizontal to vertical permeability ratio has a significant effect on the critical Rayleigh number of the three-dimensional fault system, but the horizontal to vertical thermal conductivity ratio has little influence on the convective instability of the system, and (2) if the fault material is isotropic in the fault plane, the thermal conductivity ratio of the fault normal to plane has a considerable effect on the critical Rayleigh number of the three-dimensional fault system, but the effect of the permeability ratio of the fault normal to plane on the critical Rayleigh number of three-dimensional convective flow is negligible.

Continental extension: From core complexes to rigid block faulting

Extension of overthickened continental crust is commonly characterized by an early core complex stage of extension followed by a later stage of crustal-scale rigid block faulting. These two stages are clearly recognized during the extensional destruction of the Alpine orogen in northeast Corsica, where rigid block faulting overprinting core complex formation eventually led to crustal separation and the formation of a new oceanic backarc basin (the Ligurian Sea). Here we investigate the geodynamic evolution of continental extension by using a novel, fully coupled thermomechanical numerical model of the continental crust. We consider that the dynamic evolution is governed by fault weakening, which is generated by the evolution of the natural-state variables (i.e., pressure, deviatoric stress, temperature, and strain rate) and their associated energy fluxes. Our results show the appearance of a detachment layer that controls the initial separation of the brittle crust on characteristic listric faults, and a core complex formation that is exhuming strongly deformed rocks of the detachment zone and relatively undeformed crustal cores. This process is followed by a transitional period, characterized by an apparent tectonic quiescence, in which deformation is not localized and energy stored in the upper crust is transferred downward and causes self-organized mobilization of the lower crust. Eventually, the entire crust ruptures on major crosscutting faults, shifting the tectonic regime from core complex formation to wholesale rigid block faulting.

The plausibility of long-wavelength stress correlation or stress magnitude as a mechanism for precursory seismicity: Results from two simple elastic models

Observations of accelerating seismic activity prior to large earthquakes in natural fault systems have raised hopes for intermediate-term eartquake forecasting. If this phenomena does exist, then what causes it to occur? Recent theoretical work suggests that the accelerating seismic release sequence is a symptom of increasing long-wavelength stress correlation in the fault region. A more traditional explanation, based on Reid's elastic rebound theory, argues that an accelerating sequence of seismic energy release could be a consequence of increasing stress in a fault system whose stress moment release is dominated by large events. Both of these theories are examined using two discrete models of seismicity: a Burridge-Knopoff block-slider model and an elastic continuum based model. Both models display an accelerating release of seismic energy prior to large simulated earthquakes. In both models there is a correlation between the rate of seismic energy release with the total root-mean-squared stress and the level of long-wavelength stress correlation. Furthermore, both models exhibit a systematic increase in the number of large events at high stress and high long-wavelength stress correlation levels. These results suggest that either explanation is plausible for the accelerating moment release in the models examined. A statistical model based on the Burridge-Knopoff block-slider is constructed which indicates that stress alone is sufficient to produce accelerating release of seismic energy with time prior to a large earthquake.

Adaptive multiscale principal components analysis for online monitoring of wastewater treatment

Fault detection and isolation (FDI) are important steps in the monitoring and supervision of industrial processes. Biological wastewater treatment (WWT) plants are difficult to model, and hence to monitor, because of the complexity of the biological reactions and because plant influent and disturbances are highly variable and/or unmeasured. Multivariate statistical models have been developed for a wide variety of situations over the past few decades, proving successful in many applications. In this paper we develop a new monitoring algorithm based on Principal Components Analysis (PCA). It can be seen equivalently as making Multiscale PCA (MSPCA) adaptive, or as a multiscale decomposition of adaptive PCA. Adaptive Multiscale PCA (AdMSPCA) exploits the changing multivariate relationships between variables at different time-scales. Adaptation of scale PCA models over time permits them to follow the evolution of the process, inputs or disturbances. Performance of AdMSPCA and adaptive PCA on a real WWT data set is compared and contrasted. The most significant difference observed was the ability of AdMSPCA to adapt to a much wider range of changes. This was mainly due to the flexibility afforded by allowing each scale model to adapt whenever it did not signal an abnormal event at that scale. Relative detection speeds were examined only summarily, but seemed to depend on the characteristics of the faults/disturbances. The results of the algorithms were similar for sudden changes, but AdMSPCA appeared more sensitive to slower changes.

Kinematics of oblique collision and ramping inferred from microstructures and strain in middle crustal rocks, central Southern Alps, New Zealand

The hanging wall of the Alpine Fault near Franz Josef Glacier has been exhumed during the past similar to2-3 m.y. providing a sample of the ductilely deformed middle crust of a modem obliquely convergent orogen. Presently exposed rocks of the Pacific Plate are inferred to have undergone several phases of ductile deformation as they moved westward above a mid-crustal detachment. Initially they were transpressed across the outboard part of the orogen, resulting in oblate fabrics with a down-dip stretch. Later, they encountered the Alpine Fault, experiencing an oblique-slip backshearing on vertical planes. This escalator-like deformation tilted and thinned the incoming crust onto that crustal-scale oblique ramp. This style of hanging wall deformation may affect only the most rapidly uplifting, central part of the Southern Alps because of the low flexural rigidity of the crust in that region and its displacement over a relatively sharp ramp-angle at depth. A 3D transpressive flow affected mylonites locally near the fault, but their shear direction remained parallel to plate motion, ruling out ductile 'extrusion' as an important process in this orogen. Outside the mylonite zone, late Cenozoic shortening is inferred to be modest (30-40%), as measured from deformation of younger biotite grains. Oblique collision is dominated by translation on the Alpine Fault, and rocks migrate rapidly through the deforming zone, preventing the accumulation of large finite strains. Transpression may play a minor role in oblique collision. (C) 2001 Elsevier Science Ltd. All rights reserved.