title: Stop 2 Kainui silt loam and Naike clay, Gordonton Rd

authors: Lowe, David J.

subjects: New Zealand

abstract: At this stop are several remarkable features both stratigraphic and pedological, and a “two-storied” soil, the Kainui silt loam alongside (in just a few places) the Naike clay. Both soils are Ultisols. The sequence of tephra beds and buried soil horizons spanning about 1 million years was exposed in 2007 by road works.

text: Stop 2 Kainui silt loam and Naike clay, Gordonton Rd At this stop are several remarkable features both stratigraphic and pedological, and a “two-storied” soil, the Kainui silt loam alongside (in just a few places) the Naike clay. Both soils are Ultisols. The sequence of tephra beds and buried soil horizons spanning about 1 million years was exposed in 2007 by road works.

year: 2008

title: Using classification to evaluate the output of confidence-based association rule mining

authors: Mutter, Stefan

subjects: computer science

abstract: Association rule mining is a data mining technique that reveals interesting relationships in a database. Existing approaches employ different parameters to search for interesting rules. This fact and the large number of rules make it difficult to compare the output of confidence-based association rule miners. This paper explores the use of classification performance as a metric for evaluating their output. Previous work on forming classifiers from association rules has focussed on accurate classification, whereas we concentrate on using the properties of the resulting classifiers as a basis for comparing confidence-based association rule learners. Therefore, we present experimental results on 12 UCI datasets showing that the quality of small rule sets generated by Apriori can be improved by using the predictive Apriori algorithm. We also show that CBA, the standard method for classification using association rules, is generally inferior to standard rule learners concerning both running time and size of rule sets.

text: Using classification to evaluate the output of confidence-based association rule mining Association rule mining is a data mining technique that reveals interesting relationships in a database. Existing approaches employ different parameters to search for interesting rules. This fact and the large number of rules make it difficult to compare the output of confidence-based association rule miners. This paper explores the use of classification performance as a metric for evaluating their output. Previous work on forming classifiers from association rules has focussed on accurate classification, whereas we concentrate on using the properties of the resulting classifiers as a basis for comparing confidence-based association rule learners. Therefore, we present experimental results on 12 UCI datasets showing that the quality of small rule sets generated by Apriori can be improved by using the predictive Apriori algorithm. We also show that CBA, the standard method for classification using association rules, is generally inferior to standard rule learners concerning both running time and size of rule sets.

year: 2005

title: Book review: Beyond the scene: Landscape and identity in Aotearoa New Zealand – Edited by Janet Stephenson, Mick Abbott and Jacinta Ruru

authors: Beattie, James John

subjects: book review

abstract: This article reviews the book: “Beyond the scene: Landscape and identity in Aotearoa New Zealand”, edited by Janet Stephenson, Mick Abbott and Jacinta Ruru.

text: Book review: Beyond the scene: Landscape and identity in Aotearoa New Zealand – Edited by Janet Stephenson, Mick Abbott and Jacinta Ruru This article reviews the book: “Beyond the scene: Landscape and identity in Aotearoa New Zealand”, edited by Janet Stephenson, Mick Abbott and Jacinta Ruru.

year: 2010-12