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Local and global approaches of affinity propagation clustering for large scale data 被引量:15
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作者 Ding-yin XIA Fei WU +1 位作者 Xu-qing ZHAN Yue-ting ZHUANG 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2008年第10期1373-1381,共9页
Recently a new clustering algorithm called 'affinity propagation' (AP) has been proposed, which efficiently clustered sparsely related data by passing messages between data points. However, we want to cluster ... Recently a new clustering algorithm called 'affinity propagation' (AP) has been proposed, which efficiently clustered sparsely related data by passing messages between data points. However, we want to cluster large scale data where the similarities are not sparse in many cases. This paper presents two variants of AP for grouping large scale data with a dense similarity matrix. The local approach is partition affinity propagation (PAP) and the global method is landmark affinity propagation (LAP). PAP passes messages in the subsets of data first and then merges them as the number of initial step of iterations; it can effectively reduce the number of iterations of clustering. LAP passes messages between the landmark data points first and then clusters non-landmark data points; it is a large global approximation method to speed up clustering. Experiments are conducted on many datasets, such as random data points, manifold subspaces, images of faces and Chinese calligraphy, and the results demonstrate that the two ap-proaches are feasible and practicable. 展开更多
关键词 CLUSTERING Affinity propagation large scale data Partition affinity propagation Landmark affinity propagation
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Deriving Operational Origin-Destination Matrices From Large Scale Mobile Phone Data 被引量:1
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作者 Jingtao Ma Huan Li +1 位作者 Fang Yuan Thomas Bauer 《International Journal of Transportation Science and Technology》 2013年第3期183-203,共21页
A method is presented in this work that integrates both emerging and mature data sources to estimate the operational travel demand in fine spatial and temporal resolutions.By analyzing individuals’mobility patterns r... A method is presented in this work that integrates both emerging and mature data sources to estimate the operational travel demand in fine spatial and temporal resolutions.By analyzing individuals’mobility patterns revealed from their mobile phones,researchers and practitioners are now equipped to derive the largest trip samples for a region.Because of its ubiquitous use,extensive coverage of telecommunication services and high penetration rates,travel demand can be studied continuously in fine spatial and temporal resolutions.The derived sample or seed trip matrices are coupled with surveyed commute flow data and prevalent travel demand modeling techniques to provide estimates of the total regional travel demand in the form of origindestination(OD)matrices.The methodology is evaluated in a series of real world transportation planning studies and proved its potentials in application areas such as dynamic traffic assignment modeling,integrated corridor management and online traffic simulations. 展开更多
关键词 operational origin-destination matrix large scale mobile phone data matrix correction trip imputation path-matching travel demand projection
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Large scale classification with local diversity AdaBoost SVM algorithm 被引量:5
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作者 Chang Tiantian Liu Hongwei Zhou Shuisheng 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2009年第6期1344-1350,共7页
Local diversity AdaBoost support vector machine(LDAB-SVM) is proposed for large scale dataset classification problems.The training dataset is split into several blocks firstly, and some models based on these dataset... Local diversity AdaBoost support vector machine(LDAB-SVM) is proposed for large scale dataset classification problems.The training dataset is split into several blocks firstly, and some models based on these dataset blocks are built.In order to obtain a better performance, AdaBoost is used in each model building.In the boosting iteration step, the component learners which have higher diversity and accuracy are collected via the kernel parameters adjusting.Then the local models via voting method are integrated.The experimental study shows that LDAB-SVM can deal with large scale dataset efficiently without reducing the performance of the classifier. 展开更多
关键词 ensemble learning large scale data support vector machine ADABOOST DIVERSITY local.
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