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Your search keyword '"Ting, Kai Ming"' showing total 9 results
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9 results on '"Ting, Kai Ming"'

1. Neighbourhood Contrast: A Better Means to Detect Clusters Than Density

Author

Chen, Bo, Ting, Kai Ming, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Phung, Dinh, editor, Tseng, Vincent S., editor, Webb, Geoffrey I., editor, Ho, Bao, editor, Ganji, Mohadeseh, editor, and Rashidi, Lida, editor

Published
2018
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3. LiNearN: A new approach to nearest neighbour density estimator.

Author

Wells, Jonathan R., Ting, Kai Ming, and Washio, Takashi

Subjects
*ESTIMATION theory, *LINEAR time invariant systems, *DATA analysis, *ASYMPTOTIC distribution, *ALGORITHMS
Abstract

Abstract: Despite their wide spread use, nearest neighbour density estimators have two fundamental limitations: time complexity and O(n) space complexity. Both limitations constrain nearest neighbour density estimators to small data sets only. Recent progress using indexing schemes has improved to near linear time complexity only. We propose a new approach, called LiNearN for Linear time Nearest Neighbour algorithm, that yields the first nearest neighbour density estimator having O(n) time complexity and constant space complexity, as far as we know. This is achieved without using any indexing scheme because LiNearN uses a subsampling approach for which the subsample values are significantly less than the data size. Like existing density estimators, our asymptotic analysis reveals that the new density estimator has a parameter to trade off between bias and variance. We show that algorithms based on the new nearest neighbour density estimator can easily scale up to data sets with millions of instances in anomaly detection and clustering tasks. [Copyright &y& Elsevier]

Published
2014
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9. Towards Region Discovery in Spatial Datasets

Author

Ding, Wei, Jiamthapthaksin, Rachsuda, Parmar, Rachana, Jiang, Dan, Stepinski, Tomasz F., Eick, Christoph F., Carbonell, Jaime G., editor, Siekmann, J\'org, editor, Washio, Takashi, editor, Suzuki, Einoshin, editor, Ting, Kai Ming, editor, and Inokuchi, Akihiro, editor

Published
2008
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