Daniel A. Keim

TL;DR: This paper proposes a classification of information visualization and visual data mining techniques which is based on the data type to be visualized, the visualization technique, and the interaction and distortion technique.

TL;DR: This paper examines the behavior of the commonly used L k norm and shows that the problem of meaningfulness in high dimensionality is sensitive to the value of k, which means that the Manhattan distance metric is consistently more preferable than the Euclidean distance metric for high dimensional data mining applications.

TL;DR: A new organization of the directory is introduced which uses a split algorithm minimizing overlap and additionally utilizes the concept of supernodes to keep the directory as hierarchical as possible, and at the same time to avoid splits in the directory that would result in high overlap.

TL;DR: A new algorithm to clustering in large multimedia databases called DENCLUE (DENsity-based CLUstEring) is introduced, which has a firm mathematical basis, has good clustering properties in data sets with large amounts of noise, allows a compact mathematical description of arbitrarily shaped clusters in high-dimensional data sets and is significantly faster than existing algorithms.

TL;DR: The possibilities to collect and store data increase at a faster rate than the ability to use it for making decisions, and in most applications, raw data has no value in itself; instead the authors want to extract the information contained in it.