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).

A framework for electricity price spike analysis with advanced data mining methods

There are many techniques for electricity market price forecasting. However, most of them are designed for expected price analysis rather than price spike forecasting. An effective method of predicting the occurrence of spikes has not yet been observed in the literature so far. In this paper, a data mining based approach is presented to give a reliable forecast of the occurrence of price spikes. Combined with the spike value prediction techniques developed by the same authors, the proposed approach aims at providing a comprehensive tool for price spike forecasting. In this paper, feature selection techniques are firstly described to identify the attributes relevant to the occurrence of spikes. A simple introduction to the classification techniques is given for completeness. Two algorithms: support vector machine and probability classifier are chosen to be the spike occurrence predictors and are discussed in details. Realistic market data are used to test the proposed model with promising results.

Knowledge discovery and data mining in biological databases

The new technologies for Knowledge Discovery from Databases (KDD) and data mining promise to bring new insights into a voluminous growing amount of biological data. KDD technology is complementary to laboratory experimentation and helps speed up biological research. This article contains an introduction to KDD, a review of data mining tools, and their biological applications. We discuss the domain concepts related to biological data and databases, as well as current KDD and data mining developments in biology.

Integrating attribute and space characteristics in choropleth display and spatial data mining

This paper develops an interactive approach for exploratory spatial data analysis. Measures of attribute similarity and spatial proximity are combined in a clustering model to support the identification of patterns in spatial information. Relationships between the developed clustering approach, spatial data mining and choropleth display are discussed. Analysis of property crime rates in Brisbane, Australia is presented. A surprising finding in this research is that there are substantial inconsistencies in standard choropleth display options found in two widely used commercial geographical information systems, both in terms of definition and performance. The comparative results demonstrate the usefulness and appeal of the developed approach in a geographical information system environment for exploratory spatial data analysis.

Spatial data mining for enhanced soil map modelling

The principle of using induction rules based on spatial environmental data to model a soil map has previously been demonstrated Whilst the general pattern of classes of large spatial extent and those with close association with geology were delineated small classes and the detailed spatial pattern of the map were less well rendered Here we examine several strategies to improve the quality of the soil map models generated by rule induction Terrain attributes that are better suited to landscape description at a resolution of 250 m are introduced as predictors of soil type A map sampling strategy is developed Classification error is reduced by using boosting rather than cross validation to improve the model Further the benefit of incorporating the local spatial context for each environmental variable into the rule induction is examined The best model was achieved by sampling in proportion to the spatial extent of the mapped classes boosting the decision trees and using spatial contextual information extracted from the environmental variables.

The secondary vascular system of Actinopterygii: interspecific variation in origins and investment

Vascular casts of 3 species of Chondrichthyes, 1 of Dipnoi, 1 of Chondrostei and 14 species of the Teleostei were examined by light and scanning electron microscopy in order to give a qualitative and quantitative analysis of interarterial anastomoses (iaas) that indicate the presence (or absence) of a secondary vascular system (SVS). Anastomoses were found to originate from a variety of different primary blood vessels, many of which have not been previously identified as giving rise to secondary vessels. Segmental arteries derived from the dorsal aorta and supplying body musculature were major sites of origin of the SVS, although there was considerable variation in where, in the hierarchy of arterial branching, the anastomoses occurred. The degree of investment in a SVS was species specific, with more active species having a higher degree of secondary vascularisation. This difference was quantified using an absolute count of iaas between Anguilla reinhardtii and Trachinotus baillonii. A range of general features of the SVS is also described. No evidence of iaas was found on the coeliac, mesenteric or renal circulation in any species. Evidence of iaas was lacking in the dipnoan and chondrichthyan species examined, suggesting that a SVS is restricted to Actinopterygii. The presence and distribution of a SVS does not appear to be exclusively linked to phylogenetic position, but rather to the physiological adaptation of the species.

Investment prioritization based on broadscale spatial budgeting to meet downstream targets for suspended sediment loads

On the basis of a spatially distributed sediment budget across a large basin, costs of achieving certain sediment reduction targets in rivers were estimated. A range of investment prioritization scenarios were tested to identify the most cost-effective strategy to control suspended sediment loads. The scenarios were based on successively introducing more information from the sediment budget. The relationship between spatial heterogeneity of contributing sediment sources on cost effectiveness of prioritization was investigated. Cost effectiveness was shown to increase with sequential introduction of sediment budget terms. The solution which most decreased cost was achieved by including spatial information linking sediment sources to the downstream target location. This solution produced cost curves similar to those derived using a genetic algorithm formulation. Appropriate investment prioritization can offer large cost savings because the magnitude of the costs can vary by several times depending on what type of erosion source or sediment delivery mechanism is targeted. Target settings which only consider the erosion source rates can potentially result in spending more money than random management intervention for achieving downstream targets. Coherent spatial patterns of contributing sediment emerge from the budget model and its many inputs. The heterogeneity in these patterns can be summarized in a succinct form. This summary was shown to be consistent with the cost difference between local and regional prioritization for three of four test catchments. To explain the effect for the fourth catchment, the detail of the individual sediment sources needed to be taken into account.

Darwinian grandparenting: Preferential investment in more certain kin

Studies on grandparental investment have revealed that mothers fathers are emotionally closer to their grandchildren than are fathers' mothers. In the current study, it was hypothesized that this difference is caused by the fact that fathers' mothers often have the potential to invest in genetically more certain kin (children through their daughters). To test this hypothesis, 787 participants rated their emotional closeness and exposure to their grandparents and indicated whether they had cousins through paternal and maternal aunts and uncles. Results indicated that participants felt closer to mothers' fathers than fathers' mothers only when alternate investment outlets for fathers' mothers were available. Closeness ratings to fathers fathers also were reduced when they had grandchildren through their daughters. Exposure to grandparents revealed a similar pattern of findings but did not show the same sensitivity to the presence of more certain kin and did not appear to account for the closeness ratings.

A model of migrants' remittances with human capital investment and intrafamilial transfers

This study analyzes data on migrants' remittances using a two-period theory of intergenerational transfers based on an informal, intrafamilial loan arrangement using weak altruism, a behavior between strong altruism and pure self-interest. The model provides an integrated theory of migrants' remittances, human capital investment decisions, and intrafamilial transfers applicable to low-income countries with no official pension schemes and imperfect capital markets. Propositions, derived from the theory, are tested, re-analyzing original survey data on remittances of Pacific island migrants in Sydney. When weak altruism and strong altruism yield opposite predictions, the econometric results tend to confirm the former hypothesis and invalidate the latter.

A study of the interaction between operator style and machine capability for a hydraulic mining excavator

This paper presents a case study that explores how operator digging style juxtaposes with mechanical capability for a class of hydraulic mining excavators. The relationships between actuator and digging forces are developed and these are used to identify the excavator's capability to apply forces in various directions. Two distinct modes of operation are examined to see how they relate to the mechanical capabilities of the linkage and to establish if one has merit over the other. It is found that one of these styles results in lower loading of the machine.