%0 Journal Article %A WU Xiaoquan1 %A 2 %A LI Hui1 %A 2 %A CHEN Mei1 %A 2 %A DAI Zhenyu1 %A 2 %T DRVisSys: visualization recommendation system based on attribute correlation analysis %D 2018 %R 10.3778/j.issn.1002-8331.1710-0104 %J Computer Engineering and Applications %P 251-256 %V 54 %N 7 %X Visualization is the most effective way to unleash the insights of data. However, discovering the relevant subgroup of large multidimensional data sets and mapping it into appropriate visualizations often require large amount of sophisticated iterative analysis. In order to make it easy to obtain better visualization automatically, this paper proposes DRVisSys, a system that can produce suitable visualizations according to Canonical Correlation Analysis(CCA) based attributes relevance. Furthermore, considering that the weight of attribute is different in the real world, the system uses cascaded Hidden Markov Model(HMM) to calculate the weight of attributes, which regards weight as one of criteria of non-trivial set of attributes. Besides, the system can update the visual recommendation model based on users’ feedback in order to make the recommended visualization meet the needs of users. The experiment results show that DRVisSys can quickly analyze the data and recommend the appropriate visualization to users. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.1710-0104