François Poulet

Visual data-mining strategy lies in tightly coupling the visualizations and analytical processes into one data-mining tool that takes advantage of the strengths from multiple sources. This paper presents concrete cooperation between automatic algorithms, interactive algorithms and visualization tools. The first kind of cooperation is an interactive decision tree algorithm called CIAD+. It allows the user to be helped by an automatic algorithm based on a support vector machine (SVM) to optimize the interactive split performed at the current tree node or to compute the best split in an automatic mode. This algorithm is then modified to perform an unsupervised task, the resulting clustering algorithm has the same kind of help mechanism based on another automatic algorithm (the k-means). The last effective cooperation is a visualization algorithm used to explain the results of SVM algorithm. This visualization tool is also used to view the successive planes computed by the incremental SVM algorithm.