There has been a surge in the number of large and flat data sets – data sets containing a large number of features and a relatively small number of observations – due to the growing ability to collect and store information in medical research and other fields. Hierarchical clustering is a widely used clustering tool. In hierarchical clustering, large and flat data sets may allow for a better co...

There has been a surge in the number of large and flat data sets – data sets containing a large number of features and a relatively small number of observations – due to the growing ability to collect and store information in medical research and other fields. Hierarchical clustering is a widely used clustering tool. In hierarchical clustering, large and flat data sets may allow for a better co...

In the field of pattern recognition, combining different classifiers into a robust classifier is a common approach for improving classification accuracy. Recently, this trend has also been used to improve clustering performance especially in non-hierarchical clustering approaches. Generally hierarchical clustering is preferred in comparison with the partitional clustering for applications when ...

This paper presents a clustering algorithm for dot patterns in n-dimensional space. The n-dimensional space often represents a multivariate (nf -dimensional) function in a ns-dimensional space (ns + nf = n). The proposed algorithm decomposes the clustering problem into the two lower dimensional problems. Clustering in nf -dimensional space is performed to detect the sets of dots in n-dimensiona...

For the past decade, the need of multimedia mining has increased tremendously, especially in image data due to inexpensive digital technologies and fast mounting of image data. In this paper, we, first, show an algorithm, SpIBag (Spatial Item Bag Mining), which discovers frequent spatial patterns in images. Due to the properties of image data, SpIBag considers a bag of items together with a spa...

This paper examines the problem of clustering a sequence of objects that cannot be described with a predeened list of attributes (or variables). In many applications, such a crisp representation cannot be determined. An extension of the traditionnal propositionnal formalism is thus proposed, which allows objects to be represented as a set of components. The algorithm used for clustering is brie...

We quantify the amount of information filtered by different hierarchical clustering methods on correlations between stock returns comparing the clustering structure with the underlying industrial activity classification. We apply, for the first time to financial data, a novel hierarchical clustering approach, the Directed Bubble Hierarchical Tree and we compare it with other methods including t...

We have developed a simple colorimetric sensor array that detects a wide range of volatile analytes and then applied it to the detection of toxic gases. The sensor consists of a disposable array of cross-responsive nanoporous pigments with colours that are changed by diverse chemical interactions with analytes. Although no single chemically responsive pigment is specific for any one analyte, th...

We propose a method for segmentation of ex-pository texts based on hierarchical agglomera-tive clustering. The method uses paragraphs as the basic segments for identifying hierarchical discourse structure in the text, applying lexical similarity between them as the proximity test. Linear segmentation can be induced from the identified structure through application of two simple rules. However t...

The Hans Christian Andersen (HCA) system is an example of a new generation of embodied conversational characters which are aimed to faithfully represent a familiar historical individual and carry out human-style conversation as that individual would have done had he or she lived today. A first prototype of fairytale author HCA was tested with representative users in January 2004. This paper rep...