Visual Data Mining. Overview. Apr. 24, 2007 Visual Analytics presentation Julia Nam

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Overview Visual Data Mining Apr. 24, 2007 Visual Analytics presentation Julia Nam Visual Classification: An Interactive Approach to Decision Tree Construction M. Ankerst, C. Elsen, M. Ester, H. Kriegel, University of Munich StarClass: Interactive Visual Classification Using Star Coordinates Soon Tee Teoh, Kwan-Liu Ma, University of California, Davis PaintClass: Interactive Construction, Visualization and Exploration of Decision Trees Soon Tee Teoh, Kwan-Liu Ma, University of California, Davis Decision Trees Decision Trees

Decision Tree Classifier Most classification systems are designed for minimal user intervention. Using visualization, they allows to integrate the domain knowledge of an expert in the tree construction phase. Also, they allow to explore the decision tree interactively. Visual Classification: An Interactive Approach to Decision Tree Construction (KDD 99) M. Ankerst, C. Elsen, M. Ester, H. Kriegel, University of Munich PBC (Perception Based Classification) Circle Segments They represent all values of one attribute in a segment of a circle with the proposed arrangement inside a segment. The color of pixel is determined by the class label of the object.

Interactive Classification PBC (Perception Based Classification) Experimental Evaluation Experimental Evaluation

Contribution Introduce a fully interactive method for decision tree construction based on a multidimensional visualization. Domain knowledge of an expert can be profitably included in the tree construction phase. After that, the user has a much deeper understanding of the data than just knowing the decision tree generated by an arbitrary algorithm. StarClass & PaintingClass: Interactive Construction, Visualization and Exploration of Decision Trees (Conf. On Data Mining SDM 03 & SIGKDD 03) Soon Tee Teoh, Kwan-Liu Ma University of California, Davis PaintingClass Visual Projections in PaintingClass Start with a set of training data. Every object in the set is projected and displayed visually into 2-D space. The user creates a projection that best separates the data objects belonging in different classes. Then it is partitioned by the user into regions. Repeat projection and partitioning until the user has a satisfactory decision tree. The class is leaf nodes of the tree. Star Coordinates Good at showing dimensions with numerical attributes StarClass: Interactive Visual Classification Using Start Coordinates Parallel Coordinates Good at showing dimensions with categorical attributes

Star Coordinates User can select any projection. The color of the data object is the assigned class. Star Coordinates Painting Regions Can scale or rotate an axis by moving an axis. Can generate different axis mapping. Can be zoomed by selecting region.

Building the decision Tree Parallel Coordinates To display the categorical dimensions, Painting Regions Decision Tree Visualization & Exploration Focus + Context

Decision Tree Visualization & Exploration The Objectives of PaintingClass To create a user-directed decision tree classification system To enable users to explore and visualize multidimensional data and their corresponding decision trees to improve user understanding and to gain knowledge. First Goal : Good enough Accuracy Second Goal : Knowledge Gained User can see the hierarchy of split points. They reveal which dimensions, combinations of dimensions and values are most correlated to different classes. The shape of the decision tree indicates certain characteristics of the data. Each node in the tree is itself a visual projection of a subset of the data. It can reveal patterns, clusters, shapes and outliers. Hierarchy allows the user to focus on subsets of the data.

Conclusion The user interactively edits projections of multidimensional data and paints regions to build a decision tree. Exploration of decision trees fulfills some general goals of data mining beyond classification. PaintingClass also provides several useful extensions to StarClass, the most important of which is the use of Parallel Coordinates to display categorical values so that even datasets with categorical dimensions can be classified and visualized.

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