| Download | - View accepted manuscript: An Optimal Linear Time Algorithm for Quasi-Monotonic Segmentation (PDF, 422 KiB)
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| Author | Search for: Lemire, Daniel; Search for: Brooks, Martin; Search for: Yan, Y. |
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| Format | Text, Article |
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| Conference | IEEE International Conference on Data Mining (ICDM), November 27-30, 2005, New Orleans, Louisiana, USA |
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| Abstract | Monotonicity is a simple yet significant qualitative characteristic. We consider the problem of segmenting an array in up to K segments. We want segments to be as monotonic as possible and to alternate signs. We propose a quality metric for this problem, present an optimal linear time algorithm based on novel formalism, and compare experimentally its performance to a linear time top-down regression algorithm. We show that our algorithm is faster and more accurate. Applications include pattern recognition and qualitative modeling.<br /><br />Note: A detailed version of this paper is available by contacting one of the NRC authors. |
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| Publication date | 2005 |
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| In | |
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| Language | English |
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| NRC number | NRCC 48277 |
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| NPARC number | 5763338 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 67ba23e4-431c-4a75-9ad7-475f28c1d1a7 |
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| Record created | 2009-03-29 |
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| Record modified | 2020-10-09 |
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