Abstract
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.
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