title: Tree-based Density Estimation: Algorithms and Applications

authors: Schmidberger, Gabi

subjects: machine learning

abstract: Data Mining can be seen as an extension to statistics. It comprises the preparation
of data and the process of gathering new knowledge from it. The extraction of
new knowledge is supported by various machine learning methods. Many of the
algorithms are based on probabilistic principles or use density estimations for their
computations. Density estimation has been practised in the field of statistics for
several centuries. In the simplest case, a histogram estimator, like the simple equalwidth
histogram, can be used for this task and has been shown to be a practical
tool to represent the distribution of data visually and for computation. Like other
nonparametric approaches, it can provide a flexible solution. However, flexibility
in existing approaches is generally restricted because the size of the bins is fixed 
either the width of the bins or the number of values in them. Attempts have been
made to generate histograms with a variable bin width and a variable number of
values per interval, but the computational approaches in these methods have proven
too difficult and too slow even with modern computer technology.
In this thesis new flexible histogram estimation methods are developed and tested
as part of various machine learning tasks, namely discretization, naive Bayes classification,
clustering and multiple-instance learning. Not only are the new density
estimation methods applied to machine learning tasks, they also borrow design
principles from algorithms that are ubiquitous in artificial intelligence: divide-andconquer
methods are a well known way to tackle large problems by dividing them
into small subproblems. Decision trees, used for machine learning classification,
successfully apply this approach. This thesis presents algorithms that build density
estimators using a binary split tree to cut a range of values into subranges of
varying length. No class values are required for this splitting process, making it an
unsupervised method. The result is a histogram estimator that adapts well even to
complex density functions a novel density estimation method with flexible density
estimation ability and good computational behaviour.
Algorithms are presented for both univariate and multivariate data. The univariate
histogram estimator is applied to discretization for density estimation and
also used as density estimator inside a naive Bayes classifier. The multivariate histogram,
used as the basis for a clustering method, is applied to improve the runtime
behaviour of a well-known algorithm for multiple-instance classification. Performance
in these applications is evaluated by comparing the new approaches with
existing methods.

text: Tree-based Density Estimation: Algorithms and Applications Data Mining can be seen as an extension to statistics. It comprises the preparation
of data and the process of gathering new knowledge from it. The extraction of
new knowledge is supported by various machine learning methods. Many of the
algorithms are based on probabilistic principles or use density estimations for their
computations. Density estimation has been practised in the field of statistics for
several centuries. In the simplest case, a histogram estimator, like the simple equalwidth
histogram, can be used for this task and has been shown to be a practical
tool to represent the distribution of data visually and for computation. Like other
nonparametric approaches, it can provide a flexible solution. However, flexibility
in existing approaches is generally restricted because the size of the bins is fixed 
either the width of the bins or the number of values in them. Attempts have been
made to generate histograms with a variable bin width and a variable number of
values per interval, but the computational approaches in these methods have proven
too difficult and too slow even with modern computer technology.
In this thesis new flexible histogram estimation methods are developed and tested
as part of various machine learning tasks, namely discretization, naive Bayes classification,
clustering and multiple-instance learning. Not only are the new density
estimation methods applied to machine learning tasks, they also borrow design
principles from algorithms that are ubiquitous in artificial intelligence: divide-andconquer
methods are a well known way to tackle large problems by dividing them
into small subproblems. Decision trees, used for machine learning classification,
successfully apply this approach. This thesis presents algorithms that build density
estimators using a binary split tree to cut a range of values into subranges of
varying length. No class values are required for this splitting process, making it an
unsupervised method. The result is a histogram estimator that adapts well even to
complex density functions a novel density estimation method with flexible density
estimation ability and good computational behaviour.
Algorithms are presented for both univariate and multivariate data. The univariate
histogram estimator is applied to discretization for density estimation and
also used as density estimator inside a naive Bayes classifier. The multivariate histogram,
used as the basis for a clustering method, is applied to improve the runtime
behaviour of a well-known algorithm for multiple-instance classification. Performance
in these applications is evaluated by comparing the new approaches with
existing methods.

year: 2009

title: Pasture burning post irrigation of dairy factory effluent at Edgecumbe, New Zealand

authors: Patterson, Kelli Beth

abstract: Pasture “burning” occurs when pasture turns yellow and dies back following irrigation of high strength effluent from the Fonterra Edgecumbe dairy factory. Pasture burning generally occurs intermittently during spring and continues into early summer. The objectives of my thesis are to review literature related to pasture burning and relate literature to potential causes; monitor pasture burning events and activities at Fonterra Edgecumbe dairy factory to endeavour to find potential causes of pasture burning and undertake an experiment(s) to determine the concentration at which components of the effluent cause pasture burning. 

Preliminary observations and a literature review identified multiple potential causes of pasture burning including effluent with high or low pH, high temperature, low osmotic potential/high osmolality as well as UV-radiation damage, excess nutrients, and salt build up in the soil and plant leaves. The weather at the time of irrigation and daily changes in the effluent composition may influence the severity of burning. It is likely that pasture burning is caused by a combination of effects.

The osmolality measures the concentration of a solution. Osmolality was chosen as a measure of pasture burning as the osmolality measure the solute concentration in a solution. Fresh clover (400 - 500 mmol/kg) and ryegrass (500 - 600 mmol/kg) had a similar mean symplasmic osmolality to burnt clover and ryegrass. The high strength effluent had a lower mean osmolality (217 mmol/kg) compared to fresh and burnt clover and ryegrass. If there are spikes in the concentration of effluent, the osmolality of the effluent may exceed that of the pasture causing reverse osmosis and the water in the plant cells to move out of the leaf causing dehydration. 

A pilot trial to determine if there was a relationship between osmolality and pasture burning was undertaken. Effluent was spiked with KCl and lactose solution to increase the osmolality. The pilot trial showed that as the osmolality of the effluent increased, clover and ryegrass burning increased. A main experiment was then designed to determine the cut off point for the osmolality of the effluent to prevent pasture burning. 

The main experiment was also designed to determine if younger growth was more susceptible to pasture burning. One block had progibb and urea applied to enhance the growth of the pasture.  The main experiment however, was unable to determine if new growth was more susceptible to pasture burning as the weather and nutrient content of the soil was optimal for pasture growth in both blocks.

In the pilot trial, there was a positive correlation (R2 > 0.5) between ryegrass and osmolality, pH, electrical conductivity, total magnesium, sulphate, total sulphur, total sodium, total nitrogen, dissolved reactive phosphorus, total kjeldahl nitrogen and the sodium absorption ratio.

In the main experiment, there was a positive correlation (R2 > 0.5) between clover burning and electrical conductivity, exchangeable sodium percentage, total potassium and chloride. However, there was no strong correlation between ryegrass burning and any of the effluent properties. 
When the pilot trial and main experiment results were combined, there was a positive correlation (R2 > 0.5) between clover burning and electrical conductivity, chloride, total potassium and osmolality. 

My results suggest that the effluent osmolality should not exceed 450 mmol/kg and the electrical conductivity should not exceed 1500 mS/m to prevent strong to very strong pasture burning. Effluent that exceeds either 450 mmol/kg and/or 1500 mS/m should be recirculated through the holding tanks to reduce the concentration of the “spike”.
Clover was more susceptible to severe pasture burning than ryegrass. Further research into pasture burning is required to isolate and better understand the exact cause.

text: Pasture burning post irrigation of dairy factory effluent at Edgecumbe, New Zealand Pasture “burning” occurs when pasture turns yellow and dies back following irrigation of high strength effluent from the Fonterra Edgecumbe dairy factory. Pasture burning generally occurs intermittently during spring and continues into early summer. The objectives of my thesis are to review literature related to pasture burning and relate literature to potential causes; monitor pasture burning events and activities at Fonterra Edgecumbe dairy factory to endeavour to find potential causes of pasture burning and undertake an experiment(s) to determine the concentration at which components of the effluent cause pasture burning. 

Preliminary observations and a literature review identified multiple potential causes of pasture burning including effluent with high or low pH, high temperature, low osmotic potential/high osmolality as well as UV-radiation damage, excess nutrients, and salt build up in the soil and plant leaves. The weather at the time of irrigation and daily changes in the effluent composition may influence the severity of burning. It is likely that pasture burning is caused by a combination of effects.

The osmolality measures the concentration of a solution. Osmolality was chosen as a measure of pasture burning as the osmolality measure the solute concentration in a solution. Fresh clover (400 - 500 mmol/kg) and ryegrass (500 - 600 mmol/kg) had a similar mean symplasmic osmolality to burnt clover and ryegrass. The high strength effluent had a lower mean osmolality (217 mmol/kg) compared to fresh and burnt clover and ryegrass. If there are spikes in the concentration of effluent, the osmolality of the effluent may exceed that of the pasture causing reverse osmosis and the water in the plant cells to move out of the leaf causing dehydration. 

A pilot trial to determine if there was a relationship between osmolality and pasture burning was undertaken. Effluent was spiked with KCl and lactose solution to increase the osmolality. The pilot trial showed that as the osmolality of the effluent increased, clover and ryegrass burning increased. A main experiment was then designed to determine the cut off point for the osmolality of the effluent to prevent pasture burning. 

The main experiment was also designed to determine if younger growth was more susceptible to pasture burning. One block had progibb and urea applied to enhance the growth of the pasture.  The main experiment however, was unable to determine if new growth was more susceptible to pasture burning as the weather and nutrient content of the soil was optimal for pasture growth in both blocks.

In the pilot trial, there was a positive correlation (R2 > 0.5) between ryegrass and osmolality, pH, electrical conductivity, total magnesium, sulphate, total sulphur, total sodium, total nitrogen, dissolved reactive phosphorus, total kjeldahl nitrogen and the sodium absorption ratio.

In the main experiment, there was a positive correlation (R2 > 0.5) between clover burning and electrical conductivity, exchangeable sodium percentage, total potassium and chloride. However, there was no strong correlation between ryegrass burning and any of the effluent properties. 
When the pilot trial and main experiment results were combined, there was a positive correlation (R2 > 0.5) between clover burning and electrical conductivity, chloride, total potassium and osmolality. 

My results suggest that the effluent osmolality should not exceed 450 mmol/kg and the electrical conductivity should not exceed 1500 mS/m to prevent strong to very strong pasture burning. Effluent that exceeds either 450 mmol/kg and/or 1500 mS/m should be recirculated through the holding tanks to reduce the concentration of the “spike”.
Clover was more susceptible to severe pasture burning than ryegrass. Further research into pasture burning is required to isolate and better understand the exact cause.

year: 2017

title: Examining the Professional Learning and Development of Principals and Teachers in Maori Medium.

authors: McClunie, Adrienne Heeni

subjects: Professional Development

abstract: The Resource Teacher Māori service (RTM) has been working to lift the performance of principals and teachers of Māori medium schools for over 25 years as Māori medium education has attempted to achieve the twofold aim of revitalizing the Māori language and increasing the success of Māori students in Aotearoa/New Zealand. While there have been many positive gains over that time, issues still remain in fulfilling these aims. Therefore, an important question that needs to be answered is to what extent have RTM been successful in supporting schools to realize the key aims of Māori medium education, and what issues still remain in the service they provide? 
The central aim of this research project was to examine how Resource Teachers Māori (RTM) facilitated professional development (PD) in schools, what issues they faced, and how they negotiated them. The study was a multiple case study design of three RTM, principals and teachers and used conversational interviews as the method of data gathering.
The research found that the RTM service was viewed positively by schools and had a positive influence on teacher skills and student learning outcomes. Of particular importance was the Māori language and cultural guidance the RTM provided teachers. They were also highly committed to the Kaupapa Māori philosophy and to Māori language revitalization. However, their potential was not always realized. Factors that affected the performance of RTM included having a large number of schools to service and a wide range of teacher needs.  The support the RTM received at the national and local levels was also insufficient. An implication of these findings is that the RTM service should receive greater support and guidance, but there should also be a greater degree of accountability that the service the RTM are providing is meeting school needs.

text: Examining the Professional Learning and Development of Principals and Teachers in Maori Medium. The Resource Teacher Māori service (RTM) has been working to lift the performance of principals and teachers of Māori medium schools for over 25 years as Māori medium education has attempted to achieve the twofold aim of revitalizing the Māori language and increasing the success of Māori students in Aotearoa/New Zealand. While there have been many positive gains over that time, issues still remain in fulfilling these aims. Therefore, an important question that needs to be answered is to what extent have RTM been successful in supporting schools to realize the key aims of Māori medium education, and what issues still remain in the service they provide? 
The central aim of this research project was to examine how Resource Teachers Māori (RTM) facilitated professional development (PD) in schools, what issues they faced, and how they negotiated them. The study was a multiple case study design of three RTM, principals and teachers and used conversational interviews as the method of data gathering.
The research found that the RTM service was viewed positively by schools and had a positive influence on teacher skills and student learning outcomes. Of particular importance was the Māori language and cultural guidance the RTM provided teachers. They were also highly committed to the Kaupapa Māori philosophy and to Māori language revitalization. However, their potential was not always realized. Factors that affected the performance of RTM included having a large number of schools to service and a wide range of teacher needs.  The support the RTM received at the national and local levels was also insufficient. An implication of these findings is that the RTM service should receive greater support and guidance, but there should also be a greater degree of accountability that the service the RTM are providing is meeting school needs.

year: 2013