Annotated suffix trees for text clustering
In this paper an extension of tf-idf weighting on annotated suffix tree (AST) structure is described. The new weighting scheme can be used for computing similarity between texts, which can further serve as in input to clustering algorithm. We present preliminary tests of us-ing AST for computing similarity of Russian texts and show slight im-provement in comparison to the baseline cosine similarity after applying spectral clustering algorithm.
This state-of-the-art survey is dedicated to the memory of Emmanuil Markovich Braverman (1931-1977), a pioneer in developing the machine learning theory. The 12 revised full papers and 4 short papers included in this volume were presented at the conference "Braverman Readings in Machine Learning: Key Ideas from Inception to Current State" held in Boston, MA, USA, in April 2017, commemorating the 40th anniversary of Emmanuil Braverman's decease. The papers present an overview of some of Braverman's ideas and approaches. The collection is divided in three parts. The first part bridges the past and the present. Its main contents relate to the concept of kernel function and its application to signal and image analysis as well as clustering. The second part presents a set of extensions of Braverman's work to issues of current interest both in theory and applications of machine learning. The third part includes short essays by a friend, a student, and a colleague.
The paper defines an annotated suffix tree (AST) - a data structure used to calculate and store the frequencies of all the fragments of the given string or a collection of strings. The AST is associated with a string to text scoring, which takes all fuzzy matches into account. We show how the AST and the AST scoring can be used for Natural Language Processing tasks. Copyright © by the paper's authors. Copying only for private and academic purposes.
This article represents a new technique for collaborative filtering based on pre-clustering of website usage data. The key idea involves using clustering methods to define groups of different users.
This is a textbook in data analysis. Its contents are heavily influenced by the idea that data analysis should help in enhancing and augmenting knowledge of the domain as represented by the concepts and statements of relation between them. According to this view, two main pathways for data analysis are summarization, for developing and augmenting concepts, and correlation, for enhancing and establishing relations. Visualization, in this context, is a way of presenting results in a cognitively comfortable way. The term summarization is understood quite broadly here to embrace not only simple summaries like totals and means, but also more complex summaries such as the principal components of a set of features or cluster structures in a set of entities.
The material presented in this perspective makes a unique mix of subjects from the fields of statistical data analysis, data mining, and computational intelligence, which follow different systems of presentation.
The paper describes the results of an experimental study of topic models applied to the task of single-word term extraction. The experiments encompass several probabilistic and non-probabilistic topic models and demonstrate that topic information improves the quality of term extraction, as well as NMF with KL-divergence minimization is the best among the models under study.
A vast amount of documents in the Web have duplicates, which is a challenge for developing efficient methods that would compute clusters of similar documents. In this paper we use an approach based on computing (closed) sets of attributes having large support (large extent) as clusters of similar documents. The method is tested in a series of computer experiments on large public collections of web documents and compared to other established methods and software, such as biclustering, on same datasets. Practical efficiency of different algorithms for computing frequent closed sets of attributes is compared.
A step-by-step approach to taxonomy construction is presented. On the first step, the upper layer frame of taxonomy is built manually according to educational materials. On the next steps, the frame is refined at a chosen topic using the Wikipedia category tree and articles, both cleaned of noise. Our main tool in this is a naturally defined string-to-text relevance score, based on annotated suffix trees. The relevance scoring is used at several tasks: (1) cleaning the Wikipedia tree or page set of noise; (2) allocating Wikipedia categories to taxonomy topics; (3) deciding whether an allocated category should be included as a child to the taxonomy topic, etc. The resulting fragment of taxonomy consists of three parts: the manually set upper layer topic, the adopted part of the Wikipedia category tree and Wikipedia articles as leaves. Every leaf is assigned a set of so-called descriptors; these are phrases explaining aspects of the leaf topic. The method is illustrated by its application to two domains in the area of Mathematics: (a) “Probability theory and mathematical statistics”, (b) “Numerical mathematics” (both in Russian).
This book constitutes the refereed proceedings of the 7th International Workshop on Multiple Access Communications, MACOM 2014, held in Halmstad, Sweden, in August 2014. The 12 full papers presented were carefully reviewed and selected from 22 submissions. They describe the latest advancements in the field of multiple access communications with an emphasis on reliability issues, physical layer techniques, cognitive radio, medium access control protocols, and video coding.
Technology mining (TM) helps to acquire intelligence about the evolution of research and development (R&D), technologies, products, and markets for various STI areas and what is likely to emerge in the future by identifying trends. The present chapter introduces a methodology for the identification of trends through a combination of “thematic clustering” based on the co-occurrence of terms, and “dynamic term clustering” based on the correlation of their dynamics across time. In this way, it is possible to identify and distinguish four patterns in the evolution of terms, which eventually lead to (i) weak signals of future trends, as well as (ii) emerging, (iii) maturing, and (iv) declining trends. Key trends identified are then further analyzed by looking at the semantic connections between terms identified through TM. This helps to understand the context and further features of the trend. The proposed approach is demonstrated in the field photonics as an emerging technology with a number of potential application areas.