This work presents document clustering experiments performed over noisy texts (i.e. text that have been extracted through an automatic process like speech or character recognition). The effect of recognition errors on different clustering techniques is measured through the comparison of the results obtained with clean (manually typed texts) and noisy (automatic speech transcripts affected by 30...

This paper discusses a system for automatic clustering of urban-legend texts. Urban legend (UL) is a short story set in the present day, believed by its tellers to be true and spreading spontaneously from person to person. A corpus of Polish UL texts was collected from message boards and blogs. Each text was manually assigned to one story type. The aim of the presented system is to reconstruct ...

FAÇADE (Fast and Automatic Clustering Approach to Data Engineering) is a spatial clustering tool that can discover clusters of different sizes, shapes, and densities in noisy spatial data. Compared with the existing clustering methods, FAÇADE has several advantages: first, it separates true data and noise more effectively. Second, most steps of FAÇADE are automatic. Third, it requires only O(nl...

We describe an interactive way to generate a set of clusters for a given data set. The clustering is done by constructing local histograms, which can then be used to visualize, select, and fine-tune potential cluster candidates. The accompanying algorithm can also generate clusters automatically, allowing for an automatic or semi-automatic clustering process where the user only occasionally int...

Textual data plays an important role in the modern world. The possibilities of applying data mining techniques to uncover hidden information present in large volumes of text collections is immense. The Growing Self Organizing Map (GSOM) is a highly successful member of the Self Organising Map family and has been used as a clustering and visualisation tool across wide range of disciplines to dis...

Hierarchical clustering algorithms are typically more effective in detecting the true clustering structure of a data set than partitioning algorithms. However, hierarchical clustering algorithms do not actually create clusters, but compute only a hierarchical representation of the data set. This makes them unsuitable as an automatic pre-processing step for other algorithms that operate on detec...

This paper addresses ways in which we envisage to reduce the fine-grainedness of WordNet and express in a more systematic way the relations between its numerous sense distinctions. In the EuroWordNet project, we have distinguished various automatic methods for grouping senses into more coarse-grained sense groups. These resulting clusters reflect aspects of lexical organization, displaying a va...

Hierarchical clustering algorithms are typically more effective in detecting the true clustering structure of a data set than partitioning algorithms. However, hierarchical clustering algorithms do not actually create clusters, but compute only a hierarchical representation of the data set. This makes them unsuitable as an automatic pre-processing step for other algorithms that operate on detec...

This paper puts forward a new automatic clustering algorithm based on Multi-Objective Particle Swarm Optimization and Simulated Annealing, "MOPSOSA". The proposed algorithm is capable of automatic clustering which is appropriate for partitioning datasets to a suitable number of clusters. MOPSOSA combines the features of the multi-objective based particle swarm optimization (PSO) and the Multi-O...

Abstract. This paper deals with the clustering task for Russian texts obtained using automatic speech recognition (ASR). The input for processing are recognition result for phone call recordings and manual text transcripts for these calls. We present a comparative analysis of clustering results for recognition texts and manual text transcripts, make an evaluation of how recognition quality affe...