# Active Pattern Classification for Automatic Visual Exploration of Multi-Dimensional Data
## 2 Related Work
### Automatic Pattern Identification  
There are many indicators for detecting whether a visualization contains specific data patterns. The Scagnostics approach for detecting anomalies in SPLOM (scatterplot ma trix) [34; 35] and subsequent improvements [36; 37; 38; 39; 40; 41] are representative. Following the idea of Scagnostics, researchers have proposed many other indicators for a variety of visualization techniques, such as time series [42; 43], treemap [44; 45], parallel coordinates [46; 47; 48; 49; 50], parallel sets [51], star glyphs [52], and pixel-oriented displays [53]. [PERSON] and [PERSON] [54] used ordinary statistics to select the most suitable views for showing filtered data. [PERSON] and [PERSON] [55] extended [PERSON] and [PERSON]'s framework to rank dimensions according to indicators of subsets formed by brushing in coordinated views. Visualization authoring can automatically generate visualizations that are likely to contain patterns according to the data distribution [56; 57]. Many tools, such as SEEDB [58], Autovis [59], VisPilot [60], Foresight [61], DataShot [62], zenvisage [63], etc., also depend on indicators to generate visualizations that are likely to contain patterns. Important surveys of this type of research include [64; 65; 66; 67]. Indicators, however, can only find specific pattern types [66]. In contrast, a PC can create a pattern class based on visualization examples generated on the fly and quickly discover the same class of visualizations from a large number of candidates. Neural networks have also been used in authoring visualizations. Data2 Vis [4] formulates visualization generation as a language translation problem, training an attention-based encoder-decoder network to generate Vega-lite specifications [68]. VizML [3] applies deep neural networks to predict design choices based on a corpus of existing visualizations (label) and the associated data sets (feature). DeepEye [69] uses a decision tree to evaluate the quality of visualizations. Draco [70] can find appropriate visual encodings using a learning-to-rank mode. A PC and visualization authoring focus on different stages of IDE and can be used together. More and more researchers are using machine/deep learning-based techniques to improve the efficiency and effectiveness of data exploration [71; 72; 73]. [PERSON] et al. [74] and [PERSON] et al. [75] utilized classifiers to identify tweets relevant to analysis goals from the data stream. [PERSON] et al. [76] designed a classifier-based text retrieval system. [PERSON], [PERSON], and [PERSON] [77] demonstrated the effectiveness of classic classifiers, such as KNN and SVM, in identifying meaningful data for common visual analysis tasks. However, these works treat classifiers as a preliminary data-filtering component, unlike our approach, which uses a classifier to mine data patterns directly. Many works train machine-learning models to retrieve visualizations relevant to analyst-specified patterns. [PERSON], [PERSON], and [PERSON] [78] utilized a multinomial logistic regression model to determine whether two visualizations contain similar patterns. [PERSON] et al. [79] trained a model to rank similarity indicators reflecting analysts' preferences and used the best-ranked indicator to retrieve visualizations of interest. A PC does not require analysts to specify target patterns and therefore has better applicability. Applying supervised classifiers to improve IDE automation is a newly emerging trend in visualization. [PERSON] al. [9] built a classifier to identify four traffic congestion patterns from their proposed cumulative vehicle curves. However, since they determine pattern classes before training the classifier, they do not need to consider the challenge of identifying abnormal samples to update the classifier during IDE. [PERSON] et al. [8] proposed Facetto, a visualization tool that integrates a classifier to identify cancer and immune-cell types from microscopy images of human tumors and tissues.