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The main purpose of this book is to investigate, explore and describe approaches and methods to facilitate data understanding through analytics solutions based on its principles, concepts and applications. But analyzing data is also about involving the use of software. For this, and in order to cover some aspect of data analytics, this book uses software (Excel, SPSS, Python, etc) which can help readers to better understand the analytics process in simple terms and supporting useful methods in its application.
Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains. The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as feature selection, agglomerative clustering, partitional clustering, density-based clustering, probabilistic clustering, grid-based clustering, spectral clustering, and nonnegative matrix factorization Domains, covering methods used for different domains of data, such as categorical data, text data, multimedia data, graph data, biological data, stream data, uncertain data, time series clustering, high-dimensional clustering, and big data Variations and Insights, discussing important variations of the clustering process, such as semisupervised clustering, interactive clustering, multiview clustering, cluster ensembles, and cluster validation In this book, top researchers from around the world explore the characteristics of clustering problems in a variety of application areas. They also explain how to glean detailed insight from the clustering process—including how to verify the quality of the underlying clusters—through supervision, human intervention, or the automated generation of alternative clusters.
This book lays out a path leading from the linguistic and cognitive basics, to classical rule-based and machine learning algorithms, to today’s state-of-the-art approaches, which use advanced empirically grounded techniques, automatic knowledge acquisition, and refined linguistic modeling to make a real difference in real-world applications. Anaphora and coreference resolution both refer to the process of linking textual phrases (and, consequently, the information attached to them) within as well as across sentence boundaries, and to the same discourse referent. The book offers an overview of recent research advances, focusing on practical, operational approaches and their applications. In part I (Background), it provides a general introduction, which succinctly summarizes the linguistic, cognitive, and computational foundations of anaphora processing and the key classical rule- and machine-learning-based anaphora resolution algorithms. Acknowledging the central importance of shared resources, part II (Resources) covers annotated corpora, formal evaluation, preprocessing technology, and off-the-shelf anaphora resolution systems. Part III (Algorithms) provides a thorough description of state-of-the-art anaphora resolution algorithms, covering enhanced machine learning methods as well as techniques for accomplishing important subtasks such as mention detection and acquisition of relevant knowledge. Part IV (Applications) deals with a selection of important anaphora and coreference resolution applications, discussing particular scenarios in diverse domains and distilling a best-practice model for systematically approaching new application cases. In the concluding part V (Outlook), based on a survey conducted among the contributing authors, the prospects of the research field of anaphora processing are discussed, and promising new areas of interdisciplinary cooperation and emerging application scenarios are identified. Given the book’s design, it can be used both as an accompanying text for advanced lectures in computational linguistics, natural language engineering, and computer science, and as a reference work for research and independent study. It addresses an audience that includes academic researchers, university lecturers, postgraduate students, advanced undergraduate students, industrial researchers, and software engineers.
Data Mining Algorithms is a practical, technically-oriented guide to data mining algorithms that covers the most important algorithms for building classification, regression, and clustering models, as well as techniques used for attribute selection and transformation, model quality evaluation, and creating model ensembles. The author presents many of the important topics and methodologies widely used in data mining, whilst demonstrating the internal operation and usage of data mining algorithms using examples in R.
Accountability to Associcao Brasileira De Escolas De Biblioteconomia E Documentacao
Author: Allen Kent
Publisher: CRC Press
Category: Language Arts & Disciplines
"The Encyclopedia of Library and Information Science provides an outstanding resource in 33 published volumes with 2 helpful indexes. This thorough reference set--written by 1300 eminent, international experts--offers librarians, information/computer scientists, bibliographers, documentalists, systems analysts, and students, convenient access to the techniques and tools of both library and information science. Impeccably researched, cross referenced, alphabetized by subject, and generously illustrated, the Encyclopedia of Library and Information Science integrates the essential theoretical and practical information accumulating in this rapidly growing field."
Proceedings of the Nineteenth International Conference (ICML 2002) : University of New South Wales, Sydney, Australia, July 8-12, 2002
Author: Claude Sammut
Publisher: Morgan Kaufmann
Proceedings of the annual International Conferences on Machine Learning, 1988-present. Current volume: ICML 2002: 19th International Conference on Machine Learning. Submissions are expected that describe empirical, theoretical, and cognitive-modeling research in all areas of machine learning. Submissions that present algorithms for novel learning tasks, interdisciplinary research involving machine learning, or innovative applications of machine learning techniques to challenging, real-world problems are especially encouraged.
Conference on Thermophysics in Microgravity. Conference on Commercial/Civil Next Generation Space Transportation. 22nd Symposium on Space Nuclear Power and Propulsion. Conference on Human/Robotic Technology and the National Vision for Space Exploration.
Author: Mohamed S. El-Genk
Publisher: American Institute of Physics
The proceedings of STAIF-05 feature a broad spectrum of topics on space science and technology, space exploration, space colonization; advanced propulsion concepts; space nuclear power and propulsion systems technologies; thermophysics in microgravity, advanced energy conversion technologies; next generation space transportation; high temperature materials; and high power electric propulsion. These topics span the range from basic research to the recent technology advances and hardware testing.