Abstract
Data exploration has been proved to be an efficient solution to learn interesting new insights from dataset in an intuitional way. Typically, discovering interesting patterns and objects over high-dimensional dataset is often very difficult due to its large search space. In this paper, we developed a data exploration method named Decision Analysis of Cross Clustering (DACC) based on subspace clustering. It characterize the data objects in the representation of decision trees over divided clustering subspace, which help users quickly understand the patterns of the data and then make interactive exploration easier. We conducted a series of experiments over the real-world datasets and the results showed that, DACC is superior to the representative data explorative approach in term of efficiency and accuracy, and it is applicable for interactive exploration analysis of high-dimensional data sets.
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Acknowledgment
This work was supported by the Fund by National Natural Science Foundation of China (Grant No. 61462012, No. 61562010, No. U1531246), Guizhou University Graduate Innovation Fund (Grant No. 2017081) and the Innovation Team of the Data Analysis and Cloud Service of Guizhou Province (Grant No. [2015]53), Science and Technology Project of the Department of Science and Technology in Guizhou Province (Grant No. LH [2016]7427).
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Zhao, Q., Li, H., Chen, M., Dai, Z., Zhu, M. (2019). DACC: A Data Exploration Method for High-Dimensional Data Sets. In: Silhavy, R. (eds) Artificial Intelligence and Algorithms in Intelligent Systems. CSOC2018 2018. Advances in Intelligent Systems and Computing, vol 764. Springer, Cham. https://doi.org/10.1007/978-3-319-91189-2_22
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DOI: https://doi.org/10.1007/978-3-319-91189-2_22
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