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).

Muscle fibre types and size distribution in sub-antarctic notothenioid fishes

The presumptive tonic muscles fibres of Cottoperca gobio, Champsocephalus esox, Harpagifer bispinis, Eleginops maclovinus, Patagonothen tessellata, P. cornucola and Paranotothenia magellanica stained weakly or were unstained for glycogen, lipid, succinic dehydrogenase (SDHase) and myosin ATPase (mATPase) activity. Slow, intermediate and fast twitch muscle fibres, distinguished on the basis of the pH stability of their mATPases, showed intense, moderate and low staining activity for SDHase, respectively. Slow fibres were the major component of the pectoral fin adductor profundis muscle. The proportion of different muscle fibre types varied from the proximal to distal end of the muscle, but showed relatively little variation between species. The myotomes contained a lateral superficial strip of red muscle composed of presumptive tonic, slow twitch and intermediate fibres, thickening to a major wedge at the horizontal septum. All species also had characteristic secondary dorsal and ventral wedges of red muscle. The relative abundance and localization of muscle fibre types in the red muscle varied between species and with body size in the protandric hermaphrodite E. maclovinus. The frequency distribution of diameters for fast twitch muscle fibres, the major component of deep white muscle, was determined in fish of a range of body sizes. The absence of fibres <20 mu m diameter was used as a criterion for the cessation of muscle fibre recruitment. Fibre recruitment had stopped in P, tessellata of 13.8 cm L-T and E, maclovinus of 32.8 cm L-T, equivalent to 49 and 36.5% of their recorded maximum sizes respectively. As a result in 20-cm P. tessellata, the maximum fibre diameter was 300 mu m and 36% of fibres were in excess of 200 mu m The unusually large maximum fibre diameter, the general arrangement of the red muscle layer and the extreme pH lability of the mATPase of fast twitch fibres are all common characters of the sub-Antarctic and Antarctic Notothenioids, including Cottoperca gobio, the suggested sister group to the Notothenidae. (C) 2000 The Fisheries Society of the British Isles.

Effect of honey types and concentration on starch gelatinization

The complex and variable composition of honey, depending on source, season and processing, means different honey samples could cause variation in the characteristics of the finished product. The objective of this study was to determine how the minor components present in honey affect starch gelatinization. A Rapid Visco Analyser was used to measure changes in viscosity when unmodified maize starch was gelatinized in a honey or model sugar solution. When honey was compared to equivalent blends of sugars, there was an increase in starch viscosity with increasing levels of addition. However, at the same level, honey gave a lower viscosity than the blends of sugars. Honeys from different sources (differing in pH and amylase activity) show a varied effect on starch gelatinization, with starch viscosity increasing with addition level for six of the honeys, but decreasing with increasing addition level for two honey samples. Varying the pH also produced variation in starch gelatinization patterns between honey types. Between pH 3.0 and 4.0, starch viscosity was similar for all four honey types studied, while above this pH there were differences between all honey types. As expected, starch viscosity decreased as the solution pH neared the optimum for honey amylase activity (pH 5.3-5.6), though it did not increase as the pH moved away from the honey amylase activity optimum. Differences between honey samples, and between honey and a model sugar mixture, in their effect on starch gelatinization was attributed to honey amylase activity and the composition and concentration of minor organic compounds present. Crown Copyright (C) 2003 Published by Elsevier Ltd. on behalf of Swiss Society of Food Science and Technology

Retinal neurons: cell types and coupled networks

Retinal neurons with distinct dendritic morphologies are likely to comprise different cell types, subject to three important caveats. First, it is necessary to avoid creating “artificial” cell types based on arbitrary criteria—for example, the presence of two or three primary dendrites. Second, it is essential to take into account changes in morphology with retinal eccentricity and cell density. Third, the retina contains imperfections like any natural system and a significant number of retinal neurons display aberrant morphologies or make aberrant connections that are not typical of the population as a whole. Many types of retinal ganglion cells show diverse patterns of tracer coupling, with the simplest pattern represented by the homologous coupling shown by On-Off direction-selective (DS) ganglion cells in the rabbit retina. Neighboring DS ganglion cells with a common preferred direction have regularly spaced somata and territorial dendritic fields, whereas DS ganglion cells with different preferred directions may have closely spaced somata and overlapping dendritic fields.

Dengue virus binding to human leukocyte cell lines: receptor usage differs between cell types and virus strains

Monocyte macrophages (M phi) are thought to be the principal target cells for the dengue viruses (DV), the cause of dengue fever and hemorrhagic fever. Cell attachment is mediated by the virus envelope (E) protein, but the host-cell receptors remain elusive. Currently, candidate receptor molecules include proteins, Fc receptors, glycosaminoglycans (GAGs) and lipopolysaccharide binding CD14-associated molecules. Here, we show that in addition to M phi, cells of the T- and B-cell lineages, and including cells lacking GAGs, can bind and become infected with DV. The level of virus binding varied widely between cell lines and, notably, between virus strains within a DV serotype. The latter difference may be ascribable to one or more amino acid differences in domain II of the E protein. Heparin had no significant effect on DV binding, while heparinase treatment of cells in all cases increased DV binding, further supporting the contention that GAGs are not required for DV binding and infection of human cells. In contrast to a recent report, we found that lipopolysaccharide (LPS) had either no effect or enhanced DV binding to, and infection of various human leukocyte cell lines, while in all virus-cell combinations, depletion of Ca2+/Mg2+ enhanced DV binding. This argues against involvement of beta (2) integrins in virus-host cell interactions, a conclusion in accord with the demonstration of three virus binding membrane proteins of < 75 kDa. Collectively, the results of this study question the purported exclusive importance of the E protein domain III in DV binding to host cells and point to a far more complex interaction between various target cells and, notably, individual DV strains. (C) 2001 Elsevier Science B.V. All rights reserved.

Large scale experiments on gravel and mixed beaches: Experimental procedure, data documentation and initial results

This paper provides information on the experimental set-up, data collection methods and results to date for the project Large scale modelling of coarse grained beaches, undertaken at the Large Wave Channel (GWK) of FZK in Hannover by an international group of researchers in Spring 2002. The main objective of the experiments was to provide full scale measurements of cross-shore processes on gravel and mixed beaches for the verification and further development of cross-shore numerical models of gravel and mixed sediment beaches. Identical random and regular wave tests were undertaken for a gravel beach and a mixed sand/gravel beach set up in the flume. Measurements included profile development, water surface elevation along the flume, internal pressures in the swash zone, piezometric head levels within the beach, run-up, flow velocities in the surf-zone and sediment size distributions. The purpose of the paper is to present to the scientific community the experimental procedure, a summary of the data collected, some initial results, as well as a brief outline of the on-going research being carried out with the data by different research groups. The experimental data is available to all the scientific community following submission of a statement of objectives, specification of data requirements and an agreement to abide with the GWK and EU protocols. (C) 2005 Elsevier B.V. All rights reserved.

Data mining and simulation: a grey relationship demonstration

Fuzzy data has grown to be an important factor in data mining. Whenever uncertainty exists, simulation can be used as a model. Simulation is very flexible, although it can involve significant levels of computation. This article discusses fuzzy decision-making using the grey related analysis method. Fuzzy models are expected to better reflect decision-making uncertainty, at some cost in accuracy relative to crisp models. Monte Carlo simulation is used to incorporate experimental levels of uncertainty into the data and to measure the impact of fuzzy decision tree models using categorical data. Results are compared with decision tree models based on crisp continuous data.

Public reporting of hospital outcomes based on administrative data: risks and opportunities

In the wake of findings from the Bundaberg Hospital and Forster inquiries in Queensland, periodic public release of hospital performance reports has been recommended. A process for developing and releasing such reports is being established by Queensland Health, overseen by an independent expert panel. This recommendation presupposes that public reports based on routinely collected administrative data are accurate; that the public can access, correctly interpret and act upon report contents; that reports motivate hospital clinicians and managers to improve quality of care; and that there are no unintended adverse effects of public reporting. Available research suggests that primary data sources are often inaccurate and incomplete, that reports have low predictive value in detecting outlier hospitals, and that users experience difficulty in accessing and interpreting reports and tend to distrust their findings.

Data flow and validation in workflow modelling

A complete workflow specification requires careful integration of many different process characteristics. Decisions must be made as to the definitions of individual activities, their scope, the order of execution that maintains the overall business process logic, the rules governing the discipline of work list scheduling to performers, identification of time constraints and more. The goal of this paper is to address an important issue in workflows modelling and specification, which is data flow, its modelling, specification and validation. Researchers have neglected this dimension of process analysis for some time, mainly focussing on structural considerations with limited verification checks. In this paper, we identify and justify the importance of data modelling in overall workflows specification and verification. We illustrate and define several potential data flow problems that, if not detected prior to workflow deployment may prevent the process from correct execution, execute process on inconsistent data or even lead to process suspension. A discussion on essential requirements of the workflow data model in order to support data validation is also given..