Clustering is the unsupervised classification of patterns (observations, data items, or feature
vectors) into groups (clusters). The clustering problem has been addressed in many contexts …

Abstract Information on knowledge elicitation methods is widely scattered across the fields of
psychology, business management, education, counseling, cognitive science, linguistics …

Conceptual clustering is an important way of summarizing and explaining data. However,
the recent formulation of this paradigm has allowed little exploration of conceptual clustering …

Presents a similarity-based agglomerative clustering (SBAC) algorithm that works well for
data with mixed numeric and nominal features. A similarity measure proposed by DW …

The current consensus is that most natural categories are not organized around strict
definitions (a list of singly necessary and jointly sufficient features) but rather according to a …

The data descriptions of the units are called “symbolic” when they are more complex than
standard ones, due to the fact that they contain internal variations and are structured …

Der Begriff Data Mining, im Folgenden übersetzt mit Datenmustererkennung, beschreibt die
Extraktion implizit vorhandenen, nicht trivialen und nützlichen Wissens aus großen …

Incremental systems like C obweb suffer from ordering effects: the clusters that they discover
may differ with the presentation of objects. We introduce methods that retain C obweb's …

Natural language processing will not be able to compete with traditional information retrieval
unless high-coverage techniques are developed. It is commonly agreed that a poor …

In this paper we present two protocols for asynchronous Byzantine quorum systems (BQS)
built on top of reliable channels-one for self-verifying data and the other for any data. Our …