author_facet An, Qian
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author An, Qian
spellingShingle An, Qian
Applied Mechanics and Materials
The Effective Classification Process Analysis of the Big Data in Construction Project
General Engineering
author_sort an, qian
spelling An, Qian 1662-7482 Trans Tech Publications, Ltd. General Engineering http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.1749 <jats:p>In large construction projects, a single construction features data cannot be found to use as constraint. Traditional data classification methods use multi-feature constraints. Using multiple features for construction data uniqueness expressed has excessive features to express so that affect the efficiency of the classification. This paper proposes a construction project based on discrete transform big data classification. By enhancing the features of the construction project data, use the enhanced identification as the basis data classification, so that the scope of construction data classification can be obtained. Experimental results show that use the improved algorithm to perform big data classification on massive construction projects, it can effectively improve the accuracy of classification.</jats:p> The Effective Classification Process Analysis of the Big Data in Construction Project Applied Mechanics and Materials
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title The Effective Classification Process Analysis of the Big Data in Construction Project
title_unstemmed The Effective Classification Process Analysis of the Big Data in Construction Project
title_full The Effective Classification Process Analysis of the Big Data in Construction Project
title_fullStr The Effective Classification Process Analysis of the Big Data in Construction Project
title_full_unstemmed The Effective Classification Process Analysis of the Big Data in Construction Project
title_short The Effective Classification Process Analysis of the Big Data in Construction Project
title_sort the effective classification process analysis of the big data in construction project
topic General Engineering
url http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.1749
publishDate 2014
physical 1749-1751
description <jats:p>In large construction projects, a single construction features data cannot be found to use as constraint. Traditional data classification methods use multi-feature constraints. Using multiple features for construction data uniqueness expressed has excessive features to express so that affect the efficiency of the classification. This paper proposes a construction project based on discrete transform big data classification. By enhancing the features of the construction project data, use the enhanced identification as the basis data classification, so that the scope of construction data classification can be obtained. Experimental results show that use the improved algorithm to perform big data classification on massive construction projects, it can effectively improve the accuracy of classification.</jats:p>
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description <jats:p>In large construction projects, a single construction features data cannot be found to use as constraint. Traditional data classification methods use multi-feature constraints. Using multiple features for construction data uniqueness expressed has excessive features to express so that affect the efficiency of the classification. This paper proposes a construction project based on discrete transform big data classification. By enhancing the features of the construction project data, use the enhanced identification as the basis data classification, so that the scope of construction data classification can be obtained. Experimental results show that use the improved algorithm to perform big data classification on massive construction projects, it can effectively improve the accuracy of classification.</jats:p>
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spelling An, Qian 1662-7482 Trans Tech Publications, Ltd. General Engineering http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.1749 <jats:p>In large construction projects, a single construction features data cannot be found to use as constraint. Traditional data classification methods use multi-feature constraints. Using multiple features for construction data uniqueness expressed has excessive features to express so that affect the efficiency of the classification. This paper proposes a construction project based on discrete transform big data classification. By enhancing the features of the construction project data, use the enhanced identification as the basis data classification, so that the scope of construction data classification can be obtained. Experimental results show that use the improved algorithm to perform big data classification on massive construction projects, it can effectively improve the accuracy of classification.</jats:p> The Effective Classification Process Analysis of the Big Data in Construction Project Applied Mechanics and Materials
spellingShingle An, Qian, Applied Mechanics and Materials, The Effective Classification Process Analysis of the Big Data in Construction Project, General Engineering
title The Effective Classification Process Analysis of the Big Data in Construction Project
title_full The Effective Classification Process Analysis of the Big Data in Construction Project
title_fullStr The Effective Classification Process Analysis of the Big Data in Construction Project
title_full_unstemmed The Effective Classification Process Analysis of the Big Data in Construction Project
title_short The Effective Classification Process Analysis of the Big Data in Construction Project
title_sort the effective classification process analysis of the big data in construction project
title_unstemmed The Effective Classification Process Analysis of the Big Data in Construction Project
topic General Engineering
url http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.1749