Mining; journal of the Northwest Mining Association

Publication suspended Aug. 1897-July 1899, inclusive

Mining in Northern British Columbia

The history of the settlement of the province is tied to patterns of exploration and min development. In Northern British Columbia the Cariboo goldfields provided the impetus for settlement of the region and the beginning for mining to extend into the watern and northern regions in a series of minor gold rushes. The northern half of the province has a geological diverse mineral base that supports a wide variety of mining, and a gradual improvement of exploration and mining methods due to scientific knowledge and technology provided opportunities for lode gold and base metal mines to be developed. The success of mining is based on world ore prices and competitive markets that impact the economic viability of developing a mine. Mining faces increasing pressures in the northern half of the province due to other resource values, such as tourism or protected areas, that claim and compete for a similar land base.

Effective pattern taxonomy mining in text documents

Many data mining techniques have been proposed for mining useful patterns in databases. However, how to effectively utilize discovered patterns is still an open research issue, especially in the domain of text mining. Most existing methods adopt term-based approaches. However, they all suffer from the problems of polysemy and synonymy. This paper presents an innovative technique, pattern taxonomy mining, to improve the effectiveness of using discovered patterns for finding useful information. Substantial experiments on RCV1 demonstrate that the proposed solution achieves encouraging performance.

GYAN: A methodology for rule extraction from artificial neural networks

Artificial neural network (ANN) learning methods provide a robust and non-linear approach to approximating the target function for many classification, regression and clustering problems. ANNs have demonstrated good predictive performance in a wide variety of practical problems. However, there are strong arguments as to why ANNs are not sufficient for the general representation of knowledge. The arguments are the poor comprehensibility of the learned ANN, and the inability to represent explanation structures. The overall objective of this thesis is to address these issues by: (1) explanation of the decision process in ANNs in the form of symbolic rules (predicate rules with variables); and (2) provision of explanatory capability by mapping the general conceptual knowledge that is learned by the neural networks into a knowledge base to be used in a rule-based reasoning system. A multi-stage methodology GYAN is developed and evaluated for the task of extracting knowledge from the trained ANNs. The extracted knowledge is represented in the form of restricted first-order logic rules, and subsequently allows user interaction by interfacing with a knowledge based reasoner. The performance of GYAN is demonstrated using a number of real world and artificial data sets. The empirical results demonstrate that: (1) an equivalent symbolic interpretation is derived describing the overall behaviour of the ANN with high accuracy and fidelity, and (2) a concise explanation is given (in terms of rules, facts and predicates activated in a reasoning episode) as to why a particular instance is being classified into a certain category.