C4.5: programs for machine learning
C4.5: programs for machine learning
DEVise: integrated querying and visual exploration of large datasets
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
XmdvTool: visual interactive data exploration and trend discovery of high-dimensional data sets
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Graph Visualization and Navigation in Information Visualization: A Survey
IEEE Transactions on Visualization and Computer Graphics
Information Visualization and Visual Data Mining
IEEE Transactions on Visualization and Computer Graphics
Case Study: Visualization for Decision Tree Analysis in Data Mining
INFOVIS '01 Proceedings of the IEEE Symposium on Information Visualization 2001 (INFOVIS'01)
The parallel coordinate plot in action: design and use for geographic visualization
Computational Statistics & Data Analysis - Data visualization
PaintingClass: interactive construction, visualization and exploration of decision trees
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A model and software system for coordinated and multiple views in exploratory visualization
Information Visualization - Special issue on coordinated and multiple views in exploratory visualization
Multiple foci visualisation of large hierarchies with FlexTree
Information Visualization
prefuse: a toolkit for interactive information visualization
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A framework for visual data mining of structures
ACSC '06 Proceedings of the 29th Australasian Computer Science Conference - Volume 48
A Visualization System for Space-Time and Multivariate Patterns (VIS-STAMP)
IEEE Transactions on Visualization and Computer Graphics
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Design and evaluation of visualization support to facilitate decision trees classification
International Journal of Human-Computer Studies
A survey of image classification methods and techniques for improving classification performance
International Journal of Remote Sensing
Integrated approach for the exploration of geospatial datasets: the interaction of concepts, methods and data
ADaM: a data mining toolkit for scientists and engineers
Computers & Geosciences
From visual data exploration to visual data mining: a survey
IEEE Transactions on Visualization and Computer Graphics
Proceedings of the 1st ACM SIGSPATIAL International Workshop on Data Mining for Geoinformatics
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
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Remotely sensed imagery has become increasingly important in several applications domains, such as environmental monitoring, change detection, fire risk mapping and land use, to name only a few. Several advanced image classification techniques have been developed to analyze such imagery and in particular to improve the accuracy of classifying images in the context of such applications. However, most of the proposed classifiers remain a black box to users, leaving them with little to no means to explore and thus further improve the classification process, in particular for misclassified pixel samples. In this paper, we present the concepts, design and implementation of VDM-RS, a visual data mining system for classifying remotely sensed images and exploring image classification processes. The system provides users with two classes of components. First, visual components are offered that are specific to classifying remotely sensed images and provide traditional interfaces, such as a map view and an error matrix view. Second, the decision tree classifier view provides users with the functionality to trace and explore the classification process of individual pixel samples. This feature allows users to inspect how a sample has been correctly classified using the classifier, but more importantly, it also allows for a detailed exploration of the steps in which a sample has been misclassified. The integration of these features into a coherent, user-friendly system not only helps users in getting more insights into the data, but also to better understand and subsequently improve a classifier for remotely sensed images. We demonstrate the functionality of the system's components and their interaction for classifying imagery using a hyperspectral image dataset.