Abstract
The volume of unstructured data has been growing sharply as the era of Big Data arrives. Decision tree is one of the most widely used classification models designed for structured data. Unstructured data such as text need to be converted to structured format before being analyzed using decision tree model. In this paper, we discuss how to construct decision trees for datasets containing unstructured data. For that purpose, a decision tree construction algorithm called CUST was proposed, which can directly tackle unstructured data. CUST introduces the use of splitting criteria formed by unstructured attribute values, and reduces the number of scans on datasets by designing appropriate data structures. Experiments on real-world datasets show that CUST improves the efficiency of building classifiers for unstructured data and performs as well as, if not better than existing solutions in classification accuracy.
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Gong, S., Liu, H. (2014). Constructing Decision Trees for Unstructured Data. In: Luo, X., Yu, J.X., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2014. Lecture Notes in Computer Science(), vol 8933. Springer, Cham. https://doi.org/10.1007/978-3-319-14717-8_37
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DOI: https://doi.org/10.1007/978-3-319-14717-8_37
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