Feedback-driven interactive exploration of large multidimensional data supported by visual classifier

Abstract

Abstract-The extraction of relevant and meaningful information from multivariate or high-dimensional data is a challenging problem. One reason for this is that the number of possible representations, which might contain relevant information, grows exponentially with the amount of data dimensions. Also, not all views from a possibly large view space, are potentially relevant to a given analysis task or user. Focus+Context or Semantic Zoom Interfaces can help to some extent to efficiently search for interesting views or data segments, yet they show scalability problems for very large data sets. Accordingly, users are confronted with the problem of identifying interesting views, yet the manual exploration of the entire view space becomes ineffective or even infeasible. While certain quality metrics have been proposed recently to identify potentially interesting views, these often are defined in a heuristic way and do not take into account the application or user context. We introduce a framework for a feedback-driven view exploration, inspired by relevance feedback approaches used in Information Retrieval. Our basic idea is that users iteratively express their notion of interestingness when presented with candidate views. From that expression, a model representing the user's preferences, is trained and used to recommend further interesting view candidates. A decision support system monitors the exploration process and assesses the relevance-driven search process for convergence and stability. We present an instantiation of our framework for exploration of Scatter Plot Spaces based on visual features. We demonstrate the effectiveness of this implementation by a case study on two real-world datasets. We also discuss our framework in light of design alternatives and point out its usefulness for development of user-and context-dependent visual exploration systems.

Quality Metrics for Information Visualization

“…Behrisch et al [BKSS14] present a relevance feedback approach for a user-defined notion of interestingness in Scatter Plots. Users iteratively rank presented candidate views for their perceived interestingness.…”

Peax Interactive Visual Pattern Search in Sequential Data Using Unsupervised Deep Representation Learning

“…The ranking is then used to change the query using a weighted average of the original query and the ranked nearest neighbors. A similar approach has been proposed by Behrisch et al [3] for scatter plots, where the user interactively trains a classifier based on the user's feedback to learn to capture the interestingness of scatter plot views. Recently, Dennig et al [15] proposed a system to interactively learn the best combination of feature descriptors and a distance function for pattern separability, assuming the existence and user's knowledge about the feature descriptors.…”

The State of the Art in Integrating Machine Learning into Visual Analytics

“…In their visual classification methodology, Paiva et al [PSPM15] demonstrate that effective classification models can be built when users' interactive input, for instance, to select wrongly labelled instances, can be employed to update the classification model. Along the similar lines, Behrisch et al [BKSS14] demonstrate how users' feedback on the relevance of features in classification tasks can be incorporated into decisionmaking processes. They model their process in an iterative dialogue between the user and the algorithm and name these stages as relevance feedback and model learning.…”

“…Along the similar lines, Behrisch et al . [BKSS14] demonstrate how users’ feedback on the relevance of features in classification tasks can be incorporated into decision‐making processes. They model their process in an iterative dialogue between the user and the algorithm and name these stages as relevance feedback and model learning .…”