Are We Closing the Loop Yet? Gaps in the Generalizability of VIS4ML Research

Visualization for machine learning (VIS4ML) research aims to help experts apply their prior knowledge to develop, understand, and improve the performance of machine learning models. In conceiving VIS4ML systems, researchers characterize the nature of human knowledge to support human-in-the-loop tasks, design interactive visualizations to make ML components interpretable and elicit knowledge, and evaluate the effectiveness of human-model interchange. We survey recent VIS4ML papers to assess the generalizability of research contributions and claims in enabling human-in-the-loop ML. Our results show potential gaps between the current scope of VIS4ML research and aspirations for its use in practice. We find that while papers motivate that VIS4ML systems are applicable beyond the specific conditions studied, conclusions are often overfitted to non-representative scenarios, are based on interactions with a small set of ML experts and well-understood datasets, fail to acknowledge crucial dependencies, and hinge on decisions that lack justification. We discuss approaches to close the gap between aspirations and research claims and suggest documentation practices to report generality constraints that better acknowledge the exploratory nature of VIS4ML research

Improved Visualization of Frequent Itemset Relationships Using the Minimal Spanning Tree Algorithm

Descriptive data mining techniques offer a way of extracting useful information out of large datasets and presenting it in an interpretable fashion to be used as a basis for future decisions. Since users interpret information most easily through visual means, techniques which produce concise, visually attractive results are usually preferred. We define a method, which converts transactional data into tree-like data structures, which depict important relationships between items contained in this data. The new approach we propose is offering a way to mitigate the loss of information present in previously developed algorithms, which use mined frequent itemsets and construct tree structures. We transfer the problem to the domain of graph theory and through minimal spanning tree construction achieve more informative visualizations. We highlight the new approach with comparison to previous ones by applying it on a real-life datasets – one connected to market basket data and the other from the educational domain

Basic tasks of sentiment analysis

Subjectivity detection is the task of identifying objective and subjective sentences. Objective sentences are those which do not exhibit any sentiment. So, it is desired for a sentiment analysis engine to find and separate the objective sentences for further analysis, e.g., polarity detection. In subjective sentences, opinions can often be expressed on one or multiple topics. Aspect extraction is a subtask of sentiment analysis that consists in identifying opinion targets in opinionated text, i.e., in detecting the specific aspects of a product or service the opinion holder is either praising or complaining about

Data exploration process based on the self-organizing map

With the advances in computer technology, the amount of data that is obtained from various sources and stored in electronic media is growing at exponential rates. Data mining is a research area which answers to the challange of analysing this data in order to find useful information contained therein. The Self-Organizing Map (SOM) is one of the methods used in data mining. It quantizes the training data into a representative set of prototype vectors and maps them on a low-dimensional grid. The SOM is a prominent tool in the initial exploratory phase in data mining. The thesis consists of an introduction and ten publications. In the publications, the validity of SOM-based data exploration methods has been investigated and various enhancements to them have been proposed. In the introduction, these methods are presented as parts of the data mining process, and they are compared with other data exploration methods with similar aims. The work makes two primary contributions. Firstly, it has been shown that the SOM provides a versatile platform on top of which various data exploration methods can be efficiently constructed. New methods and measures for visualization of data, clustering, cluster characterization, and quantization have been proposed. The SOM algorithm and the proposed methods and measures have been implemented as a set of Matlab routines in the SOM Toolbox software library. Secondly, a framework for SOM-based data exploration of table-format data - both single tables and hierarchically organized tables - has been constructed. The framework divides exploratory data analysis into several sub-tasks, most notably the analysis of samples and the analysis of variables. The analysis methods are applied autonomously and their results are provided in a report describing the most important properties of the data manifold. In such a framework, the attention of the data miner can be directed more towards the actual data exploration task, rather than on the application of the analysis methods. Because of the highly iterative nature of the data exploration, the automation of routine analysis tasks can reduce the time needed by the data exploration process considerably.reviewe

New Fundamental Technologies in Data Mining

The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining

An Overview of Computational Approaches for Interpretation Analysis

It is said that beauty is in the eye of the beholder. But how exactly can we characterize such discrepancies in interpretation? For example, are there any specific features of an image that makes person A regard an image as beautiful while person B finds the same image displeasing? Such questions ultimately aim at explaining our individual ways of interpretation, an intention that has been of fundamental importance to the social sciences from the beginning. More recently, advances in computer science brought up two related questions: First, can computational tools be adopted for analyzing ways of interpretation? Second, what if the "beholder" is a computer model, i.e., how can we explain a computer model's point of view? Numerous efforts have been made regarding both of these points, while many existing approaches focus on particular aspects and are still rather separate. With this paper, in order to connect these approaches we introduce a theoretical framework for analyzing interpretation, which is applicable to interpretation of both human beings and computer models. We give an overview of relevant computational approaches from various fields, and discuss the most common and promising application areas. The focus of this paper lies on interpretation of text and image data, while many of the presented approaches are applicable to other types of data as well.Comment: Preprint submitted to Digital Signal Processin