title: Random Relational Rules

authors: Anderson, Grant

subjects: relational data mining

abstract: In the field of machine learning, methods for learning from single-table data have received much more attention than those for learning from multi-table, or relational data, which are generally more computationally complex. However, a significant amount of the world's data is relational. This indicates a need for algorithms that can operate efficiently on relational data and exploit the larger body of work produced in the area of single-table techniques.

This thesis presents algorithms for learning from relational data that mitigate, to some extent, the complexity normally associated with such learning. All algorithms in this thesis are based on the generation of random relational rules. The assumption is that random rules enable efficient and effective relational learning, and this thesis presents evidence that this is indeed the case. To this end, a system for generating random relational rules is described, and algorithms using these rules are evaluated. These algorithms include direct classification, classification by propositionalisation, clustering, semi-supervised learning and generating random forests.

The experimental results show that these algorithms perform competitively with previously published results for the datasets used, while often exhibiting lower runtime than other tested systems. This demonstrates that sufficient information for classification and clustering is retained in the rule generation process and that learning with random rules is efficient.

Further applications of random rules are investigated. Propositionalisation allows single-table algorithms for classification and clustering to be applied to the resulting data, reducing the amount of relational processing required. Further results show that techniques for utilising additional unlabeled training data improve accuracy of classification in the semi-supervised setting. The thesis also develops a novel algorithm for building random forests by makingefficient use of random rules to generate trees and leaves in parallel.

text: Random Relational Rules In the field of machine learning, methods for learning from single-table data have received much more attention than those for learning from multi-table, or relational data, which are generally more computationally complex. However, a significant amount of the world's data is relational. This indicates a need for algorithms that can operate efficiently on relational data and exploit the larger body of work produced in the area of single-table techniques.

This thesis presents algorithms for learning from relational data that mitigate, to some extent, the complexity normally associated with such learning. All algorithms in this thesis are based on the generation of random relational rules. The assumption is that random rules enable efficient and effective relational learning, and this thesis presents evidence that this is indeed the case. To this end, a system for generating random relational rules is described, and algorithms using these rules are evaluated. These algorithms include direct classification, classification by propositionalisation, clustering, semi-supervised learning and generating random forests.

The experimental results show that these algorithms perform competitively with previously published results for the datasets used, while often exhibiting lower runtime than other tested systems. This demonstrates that sufficient information for classification and clustering is retained in the rule generation process and that learning with random rules is efficient.

Further applications of random rules are investigated. Propositionalisation allows single-table algorithms for classification and clustering to be applied to the resulting data, reducing the amount of relational processing required. Further results show that techniques for utilising additional unlabeled training data improve accuracy of classification in the semi-supervised setting. The thesis also develops a novel algorithm for building random forests by makingefficient use of random rules to generate trees and leaves in parallel.

year: 2008

title: Note on paramoudra-like carbonate concretions in the Urenui Formation, North Taranaki: possible plumbing system for a Late Miocene methane seep field

authors: Nelson, Campbell S.

subjects: Taranaki Basin

abstract: A reconnaissance study of calcitic and dolomitic tubular concretions in upper slope mudstone of the Late Miocene Urenui Formation exposed along the north Taranaki coastline indicates that they have a complex diagenetic history involving different phases of carbonate cementation and likely hydrofracturing associated with build up of fluid/gas pressures. The concretions resemble classical paramoudra in the European chalk, but are not siliceous and do not have a trace fossil origin. Stable oxygen and carbon isotope data suggest that the micritic carbonate cements in the Urenui paramoudra were probably sourced primarily from ascending methane fluid/gases, and that they precipitated entirely within the host mudstone below the seafloor. We suggest the paramoudra may mark the subsurface plumbing networks of a Late Miocene cold seep system, in which case they have relevance to the evolution and migration of hydrocarbons in Taranaki Basin, at this site perhaps focussed along the Taranaki Fault. The presence of dislodged and mass-emplaced paramoudra in the axial conglomerate of channels within the Urenui mudstone suggests there could be a connection between the loci of seep field development and slope failure and canyon cutting on the Late Miocene Taranaki margin.

text: Note on paramoudra-like carbonate concretions in the Urenui Formation, North Taranaki: possible plumbing system for a Late Miocene methane seep field A reconnaissance study of calcitic and dolomitic tubular concretions in upper slope mudstone of the Late Miocene Urenui Formation exposed along the north Taranaki coastline indicates that they have a complex diagenetic history involving different phases of carbonate cementation and likely hydrofracturing associated with build up of fluid/gas pressures. The concretions resemble classical paramoudra in the European chalk, but are not siliceous and do not have a trace fossil origin. Stable oxygen and carbon isotope data suggest that the micritic carbonate cements in the Urenui paramoudra were probably sourced primarily from ascending methane fluid/gases, and that they precipitated entirely within the host mudstone below the seafloor. We suggest the paramoudra may mark the subsurface plumbing networks of a Late Miocene cold seep system, in which case they have relevance to the evolution and migration of hydrocarbons in Taranaki Basin, at this site perhaps focussed along the Taranaki Fault. The presence of dislodged and mass-emplaced paramoudra in the axial conglomerate of channels within the Urenui mudstone suggests there could be a connection between the loci of seep field development and slope failure and canyon cutting on the Late Miocene Taranaki margin.

year: 2004

title: Effective teaching for indigenous and minoritized students

authors: Bishop, Russell

subjects: effective teaching profile

abstract: Te Kotahitanga is a kaupapa Māori research and professional development project currently operating in 33 mainstream secondary schools. This project was developed in response to the ongoing problem of educational disparities for Māori students, one of New Zealand's most pressing educational issues. From initial conversations with Maori students, their families, their principals and their teachers about what constituted effective teaching understandings and practice for Maori students, we developed an Effective Teaching Profile. This Profile is central to the professional development work that has been implemented in Te Kotahitanga schools with notable increases in a range of outcomes for Maori students.

text: Effective teaching for indigenous and minoritized students Te Kotahitanga is a kaupapa Māori research and professional development project currently operating in 33 mainstream secondary schools. This project was developed in response to the ongoing problem of educational disparities for Māori students, one of New Zealand's most pressing educational issues. From initial conversations with Maori students, their families, their principals and their teachers about what constituted effective teaching understandings and practice for Maori students, we developed an Effective Teaching Profile. This Profile is central to the professional development work that has been implemented in Te Kotahitanga schools with notable increases in a range of outcomes for Maori students.

year: 2010