Traditional clustering algorithms are inapplicable to many real-world problems where limited
knowledge from domain experts is available. Incorporating the domain knowledge can …

Hidden Markov models constitute a widely employed tool for sequential data modelling;
nevertheless, their use in the clustering context has been poorly investigated. In this paper a …

Clustering is one of the most common data mining tasks, used frequently for data
categorization and analysis in both industry and academia. The focus of our research is on …

Clustering is a popular technique commonly used to search for groups of similarly
expressed genes using mRNA expression data. There are many different clustering …

Motor skill learning is usually characterized by shortening of response time and performance
of faster, more stereotypical movements. However, little is known about the changes in …

We present a novel approach for embedding general metric and nonmetric spaces into
lowdimensional Euclidean spaces. As opposed to traditional multidimensional scaling …

Learning preferences is a useful task in application fields such as collaborative filtering,
information retrieval, adaptive assistants or analysis of sensory data provided by panels …

Case-base reasoning in a real-time context requires the system to output the solution to a
given problem in a predictable and usually very fast time frame. As the number of cases that …

In this paper, we present an embedding technique, called MetricMap, which is capable of
estimating distances in a pseudometric space. Given a database of objects and a distance …

Two of the most widely-used methods in machine learning for prediction and data analysis
are classification and clustering (Duda, Hart, & Stork, 2001; Mitchell, 1997). Classification is …