title: Graph-RAT programming environment

authors: McEnnis, Daniel

subjects: computer science

abstract: Graph-RAT is a new programming environment specializing in relational data mining. It incorporates a number of different techniques into a single framework for data collection, data cleaning, propositionalization, and analysis. The language is functional where algorithms are executed over arbitrary sub-graphs of the data. Analytical results can be conducted using collaborative filtering or machine learning techniques. The example algorithms are under BSD license.

text: Graph-RAT programming environment Graph-RAT is a new programming environment specializing in relational data mining. It incorporates a number of different techniques into a single framework for data collection, data cleaning, propositionalization, and analysis. The language is functional where algorithms are executed over arbitrary sub-graphs of the data. Analytical results can be conducted using collaborative filtering or machine learning techniques. The example algorithms are under BSD license.

year: 2009-08-12

title: Clustering for Classification

authors: Evans, Reuben James Emmanuel

subjects: Clustering

abstract: Advances in technology have provided industry with an array of devices for collecting data. The frequency and scale of data collection means that
there are now many large datasets being generated. To find patterns in these datasets it would be useful to be able to apply modern methods of
classification such as support vector machines. Unfortunately these methods are computationally expensive, quadratic in the number of data points in fact, so cannot be applied directly.

This thesis proposes a framework whereby a variety of clustering methods can be used to summarise datasets, that is, reduce them to a smaller but
still representative dataset so that these advanced methods can be applied. It compares the results of using this framework against using random selection on a large number of classification and regression problems. Results show that the clustered datasets are on average fifty percent smaller than the original datasets without loss of classification accuracy which is significantly better than random selection. They also show that there is no free lunch, for each dataset it is important to choose a clustering method carefully.

text: Clustering for Classification Advances in technology have provided industry with an array of devices for collecting data. The frequency and scale of data collection means that
there are now many large datasets being generated. To find patterns in these datasets it would be useful to be able to apply modern methods of
classification such as support vector machines. Unfortunately these methods are computationally expensive, quadratic in the number of data points in fact, so cannot be applied directly.

This thesis proposes a framework whereby a variety of clustering methods can be used to summarise datasets, that is, reduce them to a smaller but
still representative dataset so that these advanced methods can be applied. It compares the results of using this framework against using random selection on a large number of classification and regression problems. Results show that the clustered datasets are on average fifty percent smaller than the original datasets without loss of classification accuracy which is significantly better than random selection. They also show that there is no free lunch, for each dataset it is important to choose a clustering method carefully.

year: 2007

title: An AR memory app based on Māori mnemonic aids

authors: Taia, Ivy

subjects: computer science

abstract: Traumatic Brain Injury (TBI) is a world-wide “silent epidemic” causing a variety of memory issues for those affected. Although several software approaches aim to aid memory recollection, they are inadequate for many TBI survivors and none address aspects of cultural awareness in New Zealand. We explored design concepts of traditional Māori cultural tools / techniques and how they helped Māori people recall past information. This paper describes our research and development of Maumahara Papahou, a mobile app that uses Augmented Reality features to create a digital memory treasure box based on the concepts of Māori mnemonic aids.

text: An AR memory app based on Māori mnemonic aids Traumatic Brain Injury (TBI) is a world-wide “silent epidemic” causing a variety of memory issues for those affected. Although several software approaches aim to aid memory recollection, they are inadequate for many TBI survivors and none address aspects of cultural awareness in New Zealand. We explored design concepts of traditional Māori cultural tools / techniques and how they helped Māori people recall past information. This paper describes our research and development of Maumahara Papahou, a mobile app that uses Augmented Reality features to create a digital memory treasure box based on the concepts of Māori mnemonic aids.

year: 2018