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.

An archaeobotanical perspective on Holocene plant-use practices in lowland northern New Guinea

Existing archaeobotanical and palynological records of plant use in the northern New Guinea lowlands are reviewed in light of recent work at Kuk and theoretical refocusing on plant use practice. A practice-based approach is supported as the most useful way of investigating the highly problematical area of tropical plant food production. The existing direct record of past plant use in lowland New Guinea is considered woefully inadequate to achieve this task, as is that in Near Oceania and Island Southeast Asia. Archaeobotanical methods exist to fill the void, but full implementation requires a change in general archaeological and palaeoecological practice.

Casimir invariants and characteristic identities for gl(infinity)

A full set of (higher-order) Casimir invariants for the Lie algebra gl(infinity) is constructed and shown to be well defined in the category O-FS generated by the highest weight (unitarizable) irreducible representations with only a finite number of nonzero weight components. Moreover, the eigenvalues of these Casimir invariants are determined explicitly in terms of the highest weight. Characteristic identities satisfied by certain (infinite) matrices with entries from gl(infinity) are also determined and generalize those previously obtained for gl(n) by Bracken and Green [A. J. Bracken and H. S. Green, J. Math. Phys. 12, 2099 (1971); H. S. Green, ibid. 12, 2106 (1971)]. (C) 1997 American Institute of Physics.

Veterinary parasitology teaching in eastern Australia

There are tendencies in universities globally to change undergraduate teaching in veterinary parasitology. To be able to give considered advice to universities, faculties, governmental bodies and professional societies about a discipline and to establish how particular changes may impact on the quality of a course, is the requirement to record and review its current status. The present paper contributes toward this objective by providing a snap-shot of the veterinary parasitology courses at the Universities of Melbourne, Sydney and Queensland in eastern Australia. It includes a description of the veterinary science curriculum in each institution, and provides an outline of its veterinary parasitology course, including objectives, topics covered, course delivery, student examination procedures and course evaluation. Student contact time in veterinary parasitology during the curriculum is currently higher in Melbourne (183 h) compared with Sydney and Queensland (106-110 h). In the teaching of parasitology, Melbourne adopts a taxonomic approach (in the pre-clinical period) followed by a combined disciplinary and problem-based approach in the clinical semesters, whereas both Sydney and Queensland focus more on presenting parasites on a host species-basis followed by a problem-based approach. (C) 2002 Elsevier Science B.V. All rights reserved.

Input-driven Language Learning

Input-driven models provide an explicit and readily testable account of language learning. Although we share Ellis's view that the statistical structure of the linguistic environment is a crucial and, until recently, relatively neglected variable in language learning, we also recognize that the approach makes three assumptions about cognition and language learning that are not universally shared. The three assumptions concern (a) the language learner as an intuitive statistician, (b) the constraints on what constitute relevant surface cues, and (c) the redescription problem faced by any system that seeks to derive abstract grammatical relations from the frequency of co-occurring surface forms and functions. These are significant assumptions that must be established if input-driven models are to gain wider acceptance. We comment on these issues and briefly describe a distributed, instance-based approach that retains the key features of the input-driven account advocated by Ellis but that also addresses shortcomings of the current approaches.