The article is concerned with the problematic of Projective ART neural network (PART NN) and their use in the area of non-controlled learning for creation of cluster. The article states description of neural network PART, principle of Projective Adaptive Resonance Theory in the process of learning of neural network and describes its individual phases. In the next part the article focuses on use...

While the use of cluster features became ubiquitous in core NLP tasks, most cluster features in NLP are based on distributional similarity. We propose a new type of clustering criteria, specific to the task of part-of-speech tagging. Instead of distributional similarity, these clusters are based on the behavior of a baseline tagger when applied to a large corpus. These cluster features provide ...

An unsupervised part-of-speech (POS) tagging system that relies on graph clustering methods is described. Unlike in current state-of-the-art approaches, the kind and number of different tags is generated by the method itself. We compute and merge two partitionings of word graphs: one based on context similarity of high frequency words, another on log-likelihood statistics for words of lower fre...

Text clustering could be very useful both as an intermediate step in a large natural language processing system and as a tool in its own right. The result of a clustering algorithm is dependent on the text representation that is used. Swedish has a fairly rich morphology and a large number of homographs. This possibly leads to problems in Information Retrieval in general. We investigate the imp...

In an earlier companion paper [56] a supervised learning neural network pattern classifier called the fuzzy min-max classification neural network was described. In this sequel, the unsupervised learning pattern clustering sibling called the fuzzy min-max clustering neural network is presented. Pattern clusters are implemented here as fuzzy sets using a membership function with a hyperbox core t...

Coresets are e cient representations of data sets such that models trained on the coreset are provably competitive with models trained on the original data set. As such, they have been successfully used to scale up clustering models such as K-Means and Gaussian mixture models to massive data sets. However, until now, the algorithms and the corresponding theory were usually specific to each clus...

Consistency is a key property of all statistical procedures analyzing randomly sampled data. Surprisingly, despite decades of work, little is known about consistency of most clustering algorithms. In this paper we investigate consistency of the popular family of spectral clustering algorithms, which clusters the data with the help of eigenvectors of graph Laplacian matrices. We develop new meth...

Clustering analysis of the gene expression profiles has been used for identifying the functions of unknown genes. Fuzzy clustering method, which is one category of clustering, assigns one sample to multiple clusters as their degrees of membership. It is more appropriate for analyzing gene expression profiles because genes usually belong to multiple functional families. However, general clusteri...

Constrained clustering is a recently presented family of semisupervised learning algorithms. These methods use domain information to impose constraints over the clustering output. The way in which those constraints (typically pair-wise constraints between documents) are introduced is by designing new clustering algorithms that enforce the accomplishment of the constraints. In this paper we pres...