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Phrase Mining from Massive Text and Its Applications

effectively turn massive, unstructured text into structured representation so as to further lay the foundation for many other downstream text mining;

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Phrase Mining from Massive Text and Its Applications

effectively turn massive, unstructured text into structured representation so as to further lay the foundation for many other downstream text mining;

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Mining Structures of Factual Knowledge from Text

methodologies of mining structures of factual knowledge (e.g., entities and their relationships) from massive, unstructured text corpora. Departing from;

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Data Fusion and Data Mining for Power System Monitoring

efficient methods to cluster and visualize data collected from multiple sensors are discussed. Both linear and nonlinear data-driven mining and;

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Data Fusion and Data Mining for Power System Monitoring

efficient methods to cluster and visualize data collected from multiple sensors are discussed. Both linear and nonlinear data-driven mining and;

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Data Mining

Addresses advanced topics in one dedicated chapter: data mining trends and research frontiers, including mining rich data types (text;

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Natural Language Processing and Text Mining

Natural Language Processing and Text Mining not only discusses applications of Natural Language Processing techniques to certain Text;

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Multidimensional Mining of Massive Text Data

such multidimensional knowledge from massive text data remains a challenging task.This book presents data mining techniques that turn;

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Multidimensional Mining of Massive Text Data

such multidimensional knowledge from massive text data remains a challenging task. This book presents data mining techniques that turn;

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Advanced Data Mining and Applications

This book constitutes the proceedings of the 16th International Conference on Advanced Data Mining and Applications, ADMA 2020, held;

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Advanced Data Mining and Applications

Mining and Applications, ADMA 2013, held in Hangzhou, China, in December 2013. The 32 regular papers and 64 short papers presented in these two;

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Text Mining with MATLAB (R)

, as well as those with little-to-no experience with text mining in general, familiarize themselves with MATLAB and its complex applications;

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Text Mining

Text Mining: Applications and Theory presents thestate-of-the-art algorithms for text mining from both the academicand industrial;

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Handbook of Research on Text and Web Mining Technologies

information to make it understandable and useful. Text mining, a variation of data mining, extracts desired information from large, unstructured text;

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Advanced Data Mining and Applications

analysis; spatio-temporal data; natural language processing and text mining; data mining applications; applications; and demos.;

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Text Mining with MATLAB (R)

practitioners, as well as those with little-to-no experience with text mining in general, familiarize themselves with MATLAB and its complex;

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Mining Text Data

Text mining applications have experienced tremendous advances because of web 2.0 and social networking applications. Recent advances;

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Mining Text Data

Text mining applications have experienced tremendous advances because of web 2.0 and social networking applications. Recent advances;

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Advanced Data Mining and Applications

selected from 170 submissions. The papers were organized in topical sections named: Data Mining Foundations; Classification and Clustering Methods;

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The Text Mining Handbook

Text mining is a new and exciting area of computer science research that tries to solve the crisis of information overload by combining;

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Mining Latent Entity Structures

and relationships. In this monograph, we investigate the principles and methodologies of mining latent entity structures from massive;

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5th International Symposium on Data Mining Applications

, algorithms, models, and theory.* Text Mining: Semantic analysis and mining text in Arabic, semi-structured, streaming, multimedia data.* Framework;

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Next Generation of Data Mining

Drawn from the US National Science Foundation's Symposium on Next Generation of Data Mining and Cyber-Enabled Discovery for Innovation;

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Next Generation of Data Mining

Drawn from the US National Science Foundation's Symposium on Next Generation of Data Mining and Cyber-Enabled Discovery for Innovation;

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Data Mining and Learning Analytics

next series of chapters showcase the pedagogical applications of Educational Data Mining (EDM) and feature case studies drawn from Business;

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Data Mining

Thorough in its coverage from basic to advanced topics, this book presents the key algorithms and techniques used in data mining. An;

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Machine Learning Theoretical Foundations and Practical Applications

navigation and mining, learning through mobile data mining, text and multimedia mining through machine learning, distributed and parallel learning;

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