Mining Mobile Group Patterns Using Trajectory Approximation
碩士 === 國立中山大學 === 資訊管理學系研究所 === 92 === In this paper, we present a novel approach to mine moving object group patterns from object movement database. At first, our approaches summarize the raw data in the source object movement database into trajectories, and then discover valid 2-groups mainly from...
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ndltd-TW-092NSYS53960542015-10-13T13:05:08Z http://ndltd.ncl.edu.tw/handle/07381290224220304207 Mining Mobile Group Patterns Using Trajectory Approximation 探勘行動群組模式-利用軌跡概算 Chin-Ming Huang 黃錦銘 碩士 國立中山大學 資訊管理學系研究所 92 In this paper, we present a novel approach to mine moving object group patterns from object movement database. At first, our approaches summarize the raw data in the source object movement database into trajectories, and then discover valid 2-groups mainly from the trajectory-based object movement database. We propose two trajectory conversion methods, namely linear regression and vector conversion. We further propose a trajectory based mobile group mining algorithm that is intended to reduce the overhead of mining 2-Group Patterns. The use of trajectories allows valid 2-groups to be mined using smaller number of summarized records (in trajectory model) and examining smaller number of candidate 2-groups. Finally, we conduct series of comprehensive experiments to evaluate and compare the performances of the proposed methods with existing approaches that use source object movement database or other summarization techniques. The experimental results demonstrate the superior performance of our proposed approach. San -Yih Hwang 黃三益 2004 學位論文 ; thesis 54 en_US |
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碩士 === 國立中山大學 === 資訊管理學系研究所 === 92 === In this paper, we present a novel approach to mine moving object group patterns from object movement database. At first, our approaches summarize the raw data in the source object movement database into trajectories, and then discover valid 2-groups mainly from the trajectory-based object movement database.
We propose two trajectory conversion methods, namely linear regression and vector conversion. We further propose a trajectory based mobile group mining algorithm that is intended to reduce the overhead of mining 2-Group Patterns. The use of trajectories allows valid 2-groups to be mined using smaller number of summarized records (in trajectory model) and examining smaller number of candidate 2-groups.
Finally, we conduct series of comprehensive experiments to evaluate and compare the performances of the proposed methods with existing approaches that use source object movement database or other summarization techniques. The experimental results demonstrate the superior performance of our proposed approach.
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San -Yih Hwang |
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San -Yih Hwang Chin-Ming Huang 黃錦銘 |
author |
Chin-Ming Huang 黃錦銘 |
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Chin-Ming Huang 黃錦銘 Mining Mobile Group Patterns Using Trajectory Approximation |
author_sort |
Chin-Ming Huang |
title |
Mining Mobile Group Patterns Using Trajectory Approximation |
title_short |
Mining Mobile Group Patterns Using Trajectory Approximation |
title_full |
Mining Mobile Group Patterns Using Trajectory Approximation |
title_fullStr |
Mining Mobile Group Patterns Using Trajectory Approximation |
title_full_unstemmed |
Mining Mobile Group Patterns Using Trajectory Approximation |
title_sort |
mining mobile group patterns using trajectory approximation |
publishDate |
2004 |
url |
http://ndltd.ncl.edu.tw/handle/07381290224220304207 |
work_keys_str_mv |
AT chinminghuang miningmobilegrouppatternsusingtrajectoryapproximation AT huángjǐnmíng miningmobilegrouppatternsusingtrajectoryapproximation AT chinminghuang tànkānxíngdòngqúnzǔmóshìlìyòngguǐjīgàisuàn AT huángjǐnmíng tànkānxíngdòngqúnzǔmóshìlìyòngguǐjīgàisuàn |
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