An overview of interactive visual data mining techniques for knowledge discovery (original) (raw)

Towards the development of environments for designing visualisation support for visual data mining

2001

John W. Tukey, who made unparalleled contributions to statistics and to science in general, during his long career at Bell Labs and Princeton University, emphasized that seeing may be believing or disbelieving, but above all, data analysis involves visual, as well as statistical, understanding. Perhaps the most famous and certainly one of the oldest visual explanations in mathematics is the visual proof of the Pythagorean theorem. This proof is unusual in its brevity and its complete appropriateness to the problem. Pictures and diagrams are also used in non-geometrical parts of mathematics, mostly for psychological reasons: harnessing our ability to reason "visually" with the elements of a diagram in order to assist our more purely logical or analytical thought processes. Thus, visual reasoning approach to the area of data mining and machine learning promises to overcome some of the difficulties experienced in the comprehension of the information encoded in data sets and the models derived by other quantitative data mining methods.

Visual Data-Mining Techniques

Visualization Handbook, 2005

Never before in history has data been generated at such high volumes as it is today. Exploring and analyzing the vast volumes of data has become increasingly difficult. Information visualization and visual data mining can help to deal with the flood of information. The advantage of visual data exploration is that the user is directly involved in the data mining process. There are a large number of information visualization techniques that have been developed over the last two decades to support the exploration of large data sets. In this paper, we propose a classification of information visualization and visual data mining techniques based on the data type to be visualized, the visualization technique, and the interaction technique. We illustrate the classification using a few examples, and indicate some directions for future work.

Visual data mining: integrating machine learning with information visualization

2006

Today, the data available to tackle many scientific challenges is vast in quantity and diverse in nature. The exploration of heterogeneous information spaces requires suitable mining algorithms as well as effective visual interfaces. Most existing systems concentrate either on mining algorithms or on visualization techniques. Though visual methods developed in information visualization have been helpful, for improved understanding of a complex large high-dimensional dataset, there is a need for an effective projection of such a dataset onto a lower-dimension (2D or 3D) manifold. This paper introduces a flexible visual data mining framework which combines advanced projection algorithms developed in the machine learning domain and visual techniques developed in the information visualization domain. The framework follows Shneiderman’s mantra to provide an effective user interface. The advantage of such an interface is that the user is directly involved in the data mining process. We in...

Data Mining (Chapter 4 in Mastering The Information Age – Solving Problems with Visual Analytics)

This chapter considers data mining, which is seen as fundamental to the automated analysis components of visual analytics. Since today’s datasets are often extremely large and complex, the combination of human and automatic analysis is key to solving many information gathering tasks. Some case studies are presented which illustrate the use of knowledge discovery and data mining (KDD) in bioinformatics and climate change. The authors then pose the question of whether industry is ready for visual analytics, citing examples of the pharmaceutical, software and marketing industries. The state of the art section gives a comprehensive review of data mining/analysis tools such as statistical and mathematical tools, visual data mining tools, Web tools and packages. Some current data mining/visual analytics approaches are then described with examples from the bioinformatics and graph visualisation fields. Technical challenges specific to data mining are described such as achieving data cleaning, integration, data fusion etc. in real-time and providing the necessary infrastructure to support data mining. The challenge of integrating the human into the data process to go towards a visual analytics approach is discussed together with issues regarding its evaluation. Several opportunities are then identified, such as the need for generic tools and methods, visualisation of models and collaboration between the KDD and visualisation communities.

A Visual Data Mining Environment

2002

Abstract. It cannot be overstated that the knowledge discovery process still presents formidable challenges. One of the main issues in knowledge discovery is the need for an overall framework that can support the entire discovery process. It is worth noting the role and place of visualization in such a framework. Visualization enables or triggers the user to use his/her outstanding visual and mental capabilities, thereby gaining insight and understanding of data.

A flexible approach for visual data mining

IEEE Transactions on Visualization and Computer Graphics, 2002

ÐThe exploration of heterogenous information spaces requires suitable mining methods as well as effective visual interfaces. Most of the existing systems concentrate either on mining algorithms or on visualization techniques. This paper describes a flexible framework for Visual Data Mining which combines analytical and visual methods to achieve a better understanding of the information space. We provide several preprocessing methods for unstructured information spaces such as a flexible hierarchy generation with user controlled refinement. Moreover, we develop new visualization techniques including an intuitive Focus+Context technique to visualize complex hierarchical graphs. A special feature of our system is a new paradigm for visualizing information structures within their frame of reference.

Visual Data Mining & Modeling Techniques

2001

1.1 Abstract The visual senses for humans have a unique status, offering a very broadband channel for information flow. Visual approaches to analysis and mining attempt to take advantage of our abilities to perceive pattern and structure in visual form and to make sense of, or interpret, what we see. Visual Data Mining techniques have proven to be of high value in exploratory data analysis and they also have a high potential for Mining large databases.

A Visual Approach To Exploratory Data Mining

Review of Business Information Systems (RBIS), 2011

As the first step upon commencing an in-depth data mining analysis, students should become intimately acquainted with the data under study. In this paper, we present a methodology and set of custom tools that we have designed and developed for use in our data mining courses that allows students to efficiently and effectively accomplish this task. The tools create interactive visual presentations of the data, encouraging students to explore the data in search of patterns or relationships that would then be investigated in subsequent steps using sophisticated statistical and machine learning tools.

Visual Knowledge Generation from Data Mining Patterns for Decision-Making

International Journal of Advanced Computer Science and Applications, 2016

The visual data mining based decision support systems had already been recognized in literature. It allows users analysing large information spaces to support complex decisionmaking. Prior research provides frameworks focused on simply representing extracted patterns. In this paper, we present a new model for visually generating knowledge from these patterns and communicating it for intelligent decision-making. To prove the practicality of the proposed model, it was applied in the medical field to fight against nosocomial infections in the intensive care units.