This paper describes an experimental com- parison of three unsupervised learning algorithms that distinguish the sense of an ambiguous word in untagged text.

Abstract not found

This chapter focuses on unsupervised corpus-based methods of word sense discrimination that are knowledge-lean, and do not rely on external knowledge sources such as machine readable dictionaries, concept hierarchies, or sense-tagged text. They do not assign sense tags to words; rather, they discriminate among word meanings based on information found in unannotated corpora. This chapter reviews distributional approaches that rely on monolingual corpora and methods based on translational equivalence as found in word-aligned parallel corpora. These techniques are organized into type- and token-based approaches. The former identify sets of related words, while the latter distinguish among the senses of a word used in multiple contexts.

Abstract. We propose an association analysis-based strategy for exploration of multi-attribute spatial datasets possessing naturally arising classification. Proposed strategy, ESTATE (Exploring Spatial daTa Association patTErns), inverts such classification by interpreting different classes found in the dataset in terms of sets of discriminative patterns of its attributes. It consists of several core steps including discriminative data mining, similarity between transactional patterns, and visualization. An algorithm for calculating similarity measure between patterns is the major original contribution that facilitates summarization of discovered information and makes the entire framework practical for real life applications. Detailed description of the ESTATE framework is followed by its application to the domain of ecology using a dataset that fuses the information on geographical distribution of biodiversity of bird species across the contiguous United States with distributions of 32 environmental variables across the same area. Key words: spatial databases, association patterns, clustering, similarity measure, biodiversity 1

We summarize here the types of different clustering algorithms included in our applications and software. Existing algorithms are most often described as either statistical and algorithmic. The statistical models are primarily mixture models, including a large variety of finite mixture models (Fraley and Raftery, 2002; Banerjee et al., 2005; Quinn et al., 2006), infinite mixture models based on the Dirichlet process prior (Blei and Jordan, 2006), and mixture models (Blei, Ng and Jordan, 2003). The algorithmic approaches include methods that partition the documents directly, those that create a hierarchy of clusterings, and those which add an additional step to the clustering procedure. The methods include some which identify an exemplar document for each cluster (Kaufman and Rousseeuw, 1990; Frey and Dueck, 2007) and those which do not (Schrodt and Gerner, 1997; Shi and Malik, 2000; Ng, Jordan and Weiss, 2002; von Luxburg, 2007). The hierarchical methods can be further sub-divided into agglomerative (Hastie, Tibshirani and Friedman, 2001), divisive (Kaufman and Rousseeuw, 1990), and other hybrid methods (Gan, Ma and Wu, 2007). To use in our program, we obtain a flat partition of the documents from hierachical clustering methods. A final group includes methods which group words and documents together simulatenously (Dhillon, 2003) and those which embed the documents into lower dimensional space and then cluster (Kohonen,

For a Web download or e-book: Your use of this publication shall be governed by the terms established by the vendor at the time you acquire this publication. U.S. Government Restricted Rights Notice: Use, duplication, or disclosure of this software and related documentation by the U.S. government is subject to the Agreement with SAS Institute and the restrictions set forth in FAR 52.227-19, Commercial Computer Software-Restricted Rights (June 1987).

This Provisional PDF corresponds to the article as it appeared upon acceptance. Copyedited and fully formatted PDF and full text (HTML) versions will be made available soon. Multi-species integrative biclustering

For a Web download or e-book: Your use of this publication shall be governed by the terms established by the vendor at the time you acquire this publication. The scanning, uploading, and distribution of this book via the Internet or any other means without the permission of the publisher is illegal and punishable by law. Please purchase only authorized electronic editions and do not participate in or encourage electronic piracy of copyrighted materials. Your support of others ’ rights is appreciated. U.S. Government Restricted Rights Notice: Use, duplication, or disclosure of this software and related documentation by the

For a Web download or e-book: Your use of this publication shall be governed by the terms established by the vendor at the time you acquire this publication. U.S. Government Restricted Rights Notice: Use, duplication, or disclosure of this software and related documentation by the U.S. government is subject to the Agreement with SAS Institute and the restrictions set forth in FAR 52.227-19, Commercial Computer Software-Restricted Rights (June 1987).

Abstract not found