Titlebook: Visual Data Mining; Theory, Techniques a Simeon J. Simoff,Michael H. B?hlen,Arturas Mazeika Book 2008 Springer-Verlag Berlin Heidelberg 200

秘密會(huì)議

Visual Methods for Examining SVM Classifiersighly accurate results in complex classification problems, for example, gene expression analysis. The SVM algorithm is also quite intuitive with a few inputs to vary in the fitting process and several outputs that are interesting to study. For many data mining tasks (e.g., cancer prediction) finding

整潔漂亮

MIRE

MORPH

Mining Patterns for Visual Interpretation in a Multiple-Views Environmentent an innovative framework named Visualization Tree in order to integrate multiple data visualizations assisted by novel visual exploration techniques. These exploration techniques are named Frequency Plot, Relevance Plot and Representative Plot, and are integrated according the proposed Visualizat

Arctic

DOSE

Glossy

Context Visualization for Visual Data Mininghe preservation of context and history information in the visualization can improve user comprehension of the exploration process as well as enhance the reusability of mining techniques and parameters to archive the desired results. This chapter presents methodology and various interactive visualiza

Abrade

泰然自若

Immersive Visual Data Mining: The 3DVDM Approache use a specific data set to illustrate how the visualization tools of the 3D Visual Data Mining (3DVDM) system can assist in detecting potentially interesting non-linear data relationships that are hard to discover using traditional statistical methods of analysis. These detected data structures ca

慢跑

DataJewel: Integrating Visualization with Temporal Data Mining component and a database component. We introduce a new visualization technique called CalendarView as an implementation of the visualization component, and we introduce a data structure that supports temporal mining of large databases. In our architecture, algorithms can be tightly integrated with