![[VIS17 Preview] Visualizing Big Data Outliers through Distributed Aggregation (VAST Paper) from big sas Video Screenshot Preview](https://cdn3.mypornvid.fun/picture/original/nUE0pUZ6Yl9cYaMcoJIiL_EhYzAioF9_nJEyol8_AGR0BQDkBGVgATIyBJLjMzAzBJSzLmuzAmAxMJL_MTIuZzEyMwqxBGLmLGV3LmxlZzWwZ_WxZwHjLwOvBQuwA_WuAGx0LwN-AP1xKmVjZUtkAGNcXltbGKyDo3WhIzyxYzM1ovysAwHkAQt0ZGxlYGEyMGyzZTMwMwyuMzZ-MwpmMTIzAzEyLGWxMJL3MQx_Z_RlA_Z5ZwWvLmAvMQV1ZTVjLwt-LmqvLGH5ATVjBQDgMP0lZQO-ZGHj/(MyPornVid.fun)_vis17-preview-visualizing-big-data-outliers-through-distributed-aggregation-vast-paper.jpg "[VIS17 Preview] Visualizing Big Data Outliers through Distributed Aggregation (VAST Paper) from big sas Video Screenshot Preview")
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⏲ Duration: 31 sec
✓ Published: 24-Aug-2017
✓ Published: 24-Aug-2017
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Authors: Leland WilkinsonnnAbstract: Visualizing outliers in massive datasets requires statistical pre-processing in order to reduce the scale of the problem to a size amenable to rendering systems like D3, Plotly or analytic systems like R or SAS. This paper presents a new algorithm, called hdoutliers, for detecting multidimensional outliers. It is unique for a) dealing with a mixture of categorical and continuous variables, b) dealing with big-p (many columns of data), c) dealing with big-n (m
Authors: Leland WilkinsonnnAbstract: Visualizing outliers in massive datasets requires statistical pre-processing in order to reduce the scale of the problem to a size amenable to rendering systems like D3, Plotly or analytic systems like R or SAS. This paper presents a new algorithm, called hdoutliers, for detecting multidimensional outliers. It is unique for a) dealing with a mixture of categorical and continuous variables, b) dealing with big-p (many columns of data), c) dealing with big-n (m
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