Data representations in low dimensions such as results from unsupervised dimensionality reduction methods are often visually interpreted to find clusters of observations. To identify clusters the result must be appreciably clustered. This property of a result may be called "clusteredness". When judged visually, the appreciation of clusteredness is highly subjective. In this paper we suggest an objective way to assess clusteredness in data representations. We provide a definition of clusteredness that captures important aspects of a clustered appearance. We characterize these aspects and define the extremes rigorously. For this characterization of clusteredness we suggest an index to assess the degree of clusteredness, coined the OPTICS Cordillera. It makes only weak assumptions and is a property of the result, invariant for different partitionings or cluster assignments. We provide bounds and a normalization for the index, and prove that it represents the aspects of clusteredness. Our index is parsimonious with respect to mandatory parameters but also exible by allowing optional parameters to be tuned. The index can be used as a descriptive goodness-of-clusteredness statistic or to compare different results. For illustration we use a data set of handwritten digits which are very differently represented in two dimensions by various popular dimensionality reduction results. Empirically, observers had a hard time to visually judge the clusteredness in these representations but our index provides a clear and easy characterisation of the clusteredness of each result. (authors' abstract) / Series: Discussion Paper Series / Center for Empirical Research Methods

Verb classifications have attracted a great deal of interest in both linguistics and natural language processing (NLP). They have proved useful for important tasks and applications, including e.g. computational lexicography, parsing, word sense disambiguation, semantic role labelling, information extraction, question-answering, and machine translation (Swier and Stevenson, 2004; Dang, 2004; Shi and Mihalcea, 2005; Kipper et al., 2008; Zapirain et al., 2008; Rios et al., 2011). Particularly useful are classes which capture generalizations about a range of linguistic properties (e.g. lexical, (morpho-)syntactic, semantic), such as those proposed by Beth Levin (1993). However, full exploitation of such classes in real-world tasks has been limited because no comprehensive or domain-specific lexical classification is available. This thesis investigates how Levin-style lexical semantic classes could be learned automatically from corpus data. Automatic acquisition is cost-effective when it involves either no or minimal supervision and it can be applied to any domain of interest where adequate corpus data is available. We improve on earlier work on automatic verb clustering. We introduce new features and new clustering methods to improve the accuracy and coverage. We evaluate our methods and features on well-established cross-domain datasets in English, on a specific domain of English (the biomedical) and on another language (French), reporting promising results. Finally, our task-based evaluation demonstrates that the automatically acquired lexical classes enable new approaches to some NLP tasks (e.g. metaphor identification) and help to improve the accuracy of existing ones (e.g. argumentative zoning).

The aim of this thesis is to explore clustering algorithms of machine unsupervised learning, which can be used for image database classification by similarity. For chosen clustering algorithms is written up a theoretical basis. For better classification of used database this thesis deals with different methods of image preprocessing. With these methods the features from image are extracted. Next the thesis solves of implementation of preprocessing methods and practical application of clustering algorithms. In practical part is programmed aplication in Python programming language, which classifies the database of images into classes by similarity. The thesis tests all of used methods and at the end of the thesis is processed searches of results.