# Active Pattern Classification for Automatic Visual Exploration of Multi-Dimensional Data
## 1 Introduction  
Interactive data exploration (IDE) is designed to identify potential data patterns in a dataset [1, 2]. Typically, analysts repeatedly conduct two steps, i.e., (1) querying the dataset to generate a visualization and (2) reading the visualization to determine whether patterns exist. Although there are many methods to automate (1) by recommending the optimal visualization technique to present target data [3, 4], the subject of (2) is still human. Subjective pattern identification reduces the IDE efficiency, imposes high workloads on analysts, and may produce biased conclusions limited by analysts' cognitive capability and experiences. A recently proposed idea, that each visualization is a snapshot taken by a virtual camera around the dataset [5, 6, 7], has inspired researchers to use image classifiers that are widely used in the classification of real-world objects to identify data patterns in visualizations [8, 9]. A pattern classifier can classify an input visualization into a class representing a pattern type, as shown in Figure 1, thus transforming pattern identification into a classification task. We are confident in the performance of the PC, considering that deep learning-based classifiers outperform humans in distinguishing tiny visual differences among real-world objects that are more complex and irregular than information in visualizations. On the other hand, a PC can improve the efficiency and effectiveness of IDE. Pattern identification, which is frequently conducted in IDE and consumes manyanalysts' energy, will be taken up by PCs. Thus, analysts can pay more attention to other high-level tasks, such as controlling the exploring direction and gaining insights into the identified patterns. PCs make AI models and humans do what they are good at in IDE, avoiding the subjective pattern identification problems mentioned above. A pattern classifier differs from an image classifier significantly. Each image inherently belongs to a class. In contrast, pattern classes are artificial, and analysts know little about the patterns of a dataset before exploring it. As the analyst generates and reads visualizations, the number of recognized patterns gradually increases. Even though we can initialize a PC through manual sampling and labeling [10; 11; 12; 13], the PC inevitably classifies a newly generated visualization that contains never-encountered patterns into an existing class during IDE. This paper proposes a method for constructing and applying a PC in IDE. The method's core is an active-learning indicator, which can quantify how much a sample does not belong to any preset class (called an abnormal sample and a normal sample, vice versa). We propose an indicator based on an experiment phenomenon on classic classifiers; i.e., abnormal samples will produce more dramatic fluctuations than normal samples in classifiers' hidden layers. The indicator is computationally efficient and inherently normalized. Thus, analysts can (1) obtain a sample's indicator score before the PC assigns it to a class and (2) set a threshold to filter abnormal samples for updating the PC to cover all recognized pattern classes during IDE. To construct, use and update pattern classifiers easily and efficiently in the face of unknown data, we further propose a PC-based IDE framework called random batch visualization exploration (RBVE). It enables interactively initializing and applying a PC in IDE and makes full use of the PC's advantages in exploration efficiency. Specifically, RBVE can quickly find representative patterns within analyst-specified exploring space by randomly synthesizing queries to generate a batch of visualizations and quickly identify their patterns through PCs. Such ability to explore the whole space at once is not available with traditional human-dominated IDE. We conducted both objective and subjective experiments to evaluate the method's usability. Quantitative benchmarking against common active-learning indicators demonstrates that our indicator is more applicable in identifying abnormal samples.