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).

Transformations of taste: Americanisation, generational change and Australian cultural consumption

Researchers within the field of cultural imperialism as well as the more recently developed globalisation paradigm have tended to dwell upon the economic or corporate dimensions of global cultural flows and have been largely indifferent to the domain of the everyday cultural tastes and forms of cultural consumption that exist in particular national contexts. This article seeks to redress this focus through an examination of one particular instance of cultural imperialism, the widely held belief in ?he Americanisation of Australian society. Using data from a major research project inquiring into Australian everyday culture the article focuses on the changes in cultural tastes and preferences that are evident in three generational cohorts: contemporary young adults, a segment of the 'baby-boom' generation now in middle age, and a group of older Australians born in the years following World War I and the 1920s. The article documents a trend in which overseas influences, particularly those originating from America, appear to be increasingly shaping Australians' tastes in a wide range of cultural domains. Nevertheless, despite these changes in cultural taste Australians of ail ages retain a strong sense of a distinctive national identity. Such findings have implications for an understanding of cultural globalisation as a process of hybridisation and intermixing.

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.

Vascular leakage stimulates phenotype alteration in ocular cells, contributing to the pathology of proliferative vitreoretinopathy

Plasma leaking from damaged retinal blood vessels can have a significant impact on the pathologies of the posterior segment of the eye. Inflammation in the eye and metabolic change resulting from diabetes mellitus causes vascular leakage with alteration of the phenotype of retinal pigment epithelial (RPE) cells and fibrocytes, resulting in changes in cell function. Phenotypically altered cells then significantly contribute to the pathogenesis of retinopathies by being incorporated into tractional membranes in the vitreous, where they secrete matrix molecules, such as fibronectin, and express altered cell surface antigens. We hypothesize that there is a direct relationship between the leaking of plasma and the proliferation and phenotypic change of RPE cells and fibroblasts, thus exacerbating the pathology of retinal disease. If the hypothesis is correct, control of vascular leakage becomes an important target of therapy in proliferative vitreoretinopathy.