Inventing Discovery Tools: Combining Information Visualization with Data Mining1

@article{Shneiderman2001InventingDT,
  title={Inventing Discovery Tools: Combining Information Visualization with Data Mining1},
  author={Ben Shneiderman},
  journal={Information Visualization},
  year={2001},
  volume={1},
  pages={12 - 5}
}
  • B. Shneiderman
  • Published 25 November 2001
  • Computer Science
  • Information Visualization
The growing use of information visualization tools and data mining algorithms stems from two separate lines of research. Information visualization researchers believe in the importance of giving users an overview and insight into the data distributions, while data mining researchers believe that statistical algorithms and machine learning can be relied on to find the interesting patterns. This paper discusses two issues that influence design of discovery tools: statistical algorithms vs visual… 

Visualizing Association Rules in a Framework for Visual Data Mining

  • P. BuonoM. Costabile
  • Computer Science
    From Integrated Publication and Information Systems to Virtual Information and Knowledge Environments
  • 2005
A visual strategy that exploits a graph-based technique and parallel coordinates to visualize the results of association rule mining algorithms helps data miners to get an overview of the rule set they are interacting with and enables them to deeper investigate inside a specific set of rules.

Combining visual techniques for Association Rules exploration

This paper presents a visual strategy to face this drawback by exploiting graph-based technique and parallel coordinates to visualize the results of association rules mining algorithms.

Hypothesis oriented cluster analysis in data mining by visualization

A novel approach, Hypothesis Oriented Verification and Validation by Visualization, named HOV3, which projects datasets based on given hypotheses by visualization in 2D space to assist the user in discovering more precise cluster information from high-dimensional datasets efficiently and effectively.

Nugget Browser: Visual Subgroup Mining and Statistical Significance Discovery in Multivariate Datasets

A novel subgroup pattern extraction and visualization system, called the Nugget Browser, that takes advantage of both data mining methods and interactive visual exploration and helps analysts in exploring their discoveries and understanding the relationships among patterns.

A Prediction-Based Visual Approach for Cluster Exploration and Cluster Validation by HOV3

With the quantified contrast between grouped data distributions produced by HOV3, users can detect clusters and verify their validity efficiently and explore and verify clustering results with quantified measurements.

Visualizing Distributions and Classification Accuracy

  • D. Groth
  • Computer Science
    Tenth International Conference on Information Visualisation (IV'06)
  • 2006
This paper utilizes straightforward histogram-based visualizations to gain insight into how the performance of a well-studied data mining technique, the naive-Bayes classifier, performs under various discretization schemes for both continuous and discrete values.

Visual Analysis and Knowledge Discovery for Text

It is argued that visual analysis, in combination with automatic knowledge discovery methods, provides several advantages, besides introducing human knowledge and visual pattern recognition into the analytical process, it provides the possibility to improve the performance of automatic methods through user feedback.

A Work-Centered Visual Analytics Model to Support Engineering Design with Interactive Visualization and Data-Mining

This paper designs and implements a specific system prototype, Learning-based Interactive Visualization for Engineering design (LIVE), for engineering designers to handle overwhelming information such as numerous design alternatives generated from automatic simulating software.

Towards Effective Visual Data Mining with Cooperative Approaches

  • F. Poulet
  • Computer Science
    Visual Data Mining
  • 2008
This work presents concrete cooperation between automatic algorithms, interactive algorithms and visualization methods and presents methods using both automatic and interactive methods to deal with very large datasets.

Analysis of High Dimension Clustered Data using Visualization Technique

A distinct approach to cluster analysis, Hypothesis Oriented Verification and Validation by Visualization, named HOV3 is introduced; it gathers datasets on a hypothesis by visualization in 2D space and can reside the user to discover more cluster information from high dimensional data sets in a productive and organized way.
...

References

SHOWING 1-10 OF 28 REFERENCES

Information Visualization in Data Mining and Knowledge Discovery

An algorithm for clustering spatial–temporal data and an overall goal to extract information from a data set and transform the information into a comprehensible structure for further use.

Data Mining Solutions: Methods and Tools for Solving Real-World Problems

This book discusses data mining techniques, techniques and tools used in the field, and future trends in Visual Data Mining.

Interactive Exploration of Time Series Data

This paper introduces timeboxes: a powerful direct-manipulation metaphor for the specification of queries over time series datasets that supports interactive formulation and modification of queries, thus speeding the process of exploring time series data sets and guiding data mining.

Data mining: practical machine learning tools and techniques with Java implementations

This presentation discusses the design and implementation of machine learning algorithms in Java, as well as some of the techniques used to develop and implement these algorithms.

The dynamic HomeFinder: evaluating dynamic queries in a real-estate information exploration system

A new concept for visualizing and searching databases utilizing direct manipulation called dynamic queries, which allows users to formulate queries by adjusting graphical widgets, such as sliders, and see the results immediately, is designed and implemented.

Towards an effective cooperation of the user and the computer for classification

A new user-centered approach to decision tree construction where the user and the computer can both contribute their strengths: the user provides domain knowledge and evaluates intermediate results of the algorithm, the computer automatically creates patterns satisfying user constraints and generates appropriate visualizations of these patterns.

Readings in information visualization - using vision to think

This paper presents a meta-anatomy of human interaction in the context of data mapping, which aims to provide a scaffolding for future generations to think and act in a more holistic way.

Dynamic queries for visual information seeking

The author discusses how experts may benefit from visual interfaces because they will be able to formulate more complex queries and interpret intricate results.

Interactive machine learning: letting users build classifiers

It is shown that appropriate techniques can empower users to create models that compete with classifiers built by state-of-the-art learning algorithms, and that small expert-defined models offer the additional advantage that they will generally be more intelligible than those generated by automatic techniques.

The Design of Experiments

  • J. I
  • Economics
    Nature
  • 1936
AbstractREADERS of “Statistical Methods for Research Workers” will welcome Prof. Fisher's new book, which is partly devoted to a development of the logical ideas underlying the earlier volume and