title: Mining Arbitrarily Large Datasets Using Heuristic k-Nearest Neighbour Search

authors: Wu, Xing

subjects: computer science

abstract: Nearest Neighbour Search (NNS) is one of the top ten data mining algorithms. It is simple and effective but has a time complexity that is the product of the number of instances and the number of dimensions. When the number of dimensions is greater than two there are no known solutions that can guarantee a sublinear retrieval time. This paper describes and evaluates two ways to make NNS efficient for datasets that are arbitrarily large in the number of instances and dimensions. The methods are best described as heuristic as they are neither exact nor approximate. Both stem from recent developments in the field of data stream classification. The first uses Hoeffding Trees, an extension of decision trees to streams and the second is a direct stream extension of NNS. The methods are evaluated in terms of their accuracy and the time taken to find the neighbours. Results show that the methods are competitive with NNS in terms of accuracy but significantly faster.

text: Mining Arbitrarily Large Datasets Using Heuristic k-Nearest Neighbour Search Nearest Neighbour Search (NNS) is one of the top ten data mining algorithms. It is simple and effective but has a time complexity that is the product of the number of instances and the number of dimensions. When the number of dimensions is greater than two there are no known solutions that can guarantee a sublinear retrieval time. This paper describes and evaluates two ways to make NNS efficient for datasets that are arbitrarily large in the number of instances and dimensions. The methods are best described as heuristic as they are neither exact nor approximate. Both stem from recent developments in the field of data stream classification. The first uses Hoeffding Trees, an extension of decision trees to streams and the second is a direct stream extension of NNS. The methods are evaluated in terms of their accuracy and the time taken to find the neighbours. Results show that the methods are competitive with NNS in terms of accuracy but significantly faster.

year: 2008

title: Radial evolution of cross helicity in high-latitude solar wind

authors: Breech, Ben

subjects: solar wind

abstract: We employ a turbulence transport theory to explain the high-latitude radial evolution of cross helicity, or Alfvénicity, observed by the Ulysses spacecraft. Evolution is slower than at low latitudes due to weakened shear driving.

text: Radial evolution of cross helicity in high-latitude solar wind We employ a turbulence transport theory to explain the high-latitude radial evolution of cross helicity, or Alfvénicity, observed by the Ulysses spacecraft. Evolution is slower than at low latitudes due to weakened shear driving.

year: 2005

title: Old bones tell new tales

authors: Lowe, David J.

subjects: New Zealand

abstract: Of all the so-called evidence that has been presented in support of human settlement in New Zealand before the second millennium, only a set of radiocarbon-dated rat bones has appeared scientifically credible. Now even that is coming under close scrutiny.

text: Old bones tell new tales Of all the so-called evidence that has been presented in support of human settlement in New Zealand before the second millennium, only a set of radiocarbon-dated rat bones has appeared scientifically credible. Now even that is coming under close scrutiny.

year: 2006