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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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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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Advanced Text Mining and Applied Principles

been formulated, the text mining research field continues to expand for the needs emerging from distinct application fields. This book provides;

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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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Damage and Failure of Interfaces

: from nanostructures in micro-electronics to macro-size structures in mining engineering. This text investigates these problems.;

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Text Mining

This book discusses text mining and different ways this type of data mining can be used to find implicit knowledge from text collections;

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Engineered Rock Structures in Mining and Civil Construction

The book collates and sifts a vast amount of literature on the design of structures in the mining and construction industries to synthesize;

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Engineered Rock Structures in Mining and Civil Construction

The book collates and sifts a vast amount of literature on the design of structures in the mining and construction industries to synthesize;

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Advances in Knowledge Discovery and Data Mining

data mining of specialized data; Part II: Classical data mining; data mining theory and principles; recommender systems; and text;

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Advances in Knowledge Discovery and Data Mining

data mining of specialized data; Part II: Classical data mining; data mining theory and principles; recommender systems; and text;

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Advances in Knowledge Discovery and Data Mining

data mining of specialized data; Part II: Classical data mining; data mining theory and principles; recommender systems; and text;

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Conceptual Structures in Practice

conceptual relations as the foundation for research in knowledge representation and knowledge mining. It includes contributions from leading;

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Conceptual Structures in Practice

conceptual relations as the foundation for research in knowledge representation and knowledge mining. It includes contributions from leading;

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Advances in Databases and Information Systems

from theoretical foundations to optimizing index structures. They focus on data mining and machine learning, data warehouses and big data;

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Beyond Databases, Architectures and Structures. Paving the Road to Smart Data Processing and Analysis

This book constitutes the refereed proceedings of the 15th International Conference entitled Beyond Databases, Architectures and Structures;

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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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Advances in Knowledge Discovery and Data Mining

supervised learning; text and opinion mining; spatio-temporal and stream data mining; factor and tensor analysis; healthcare, bioinformatics and;

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Advances in Knowledge Discovery and Data Mining

supervised learning; text and opinion mining; spatio-temporal and stream data mining; factor and tensor analysis; healthcare, bioinformatics and;

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Advances in Knowledge Discovery and Data Mining

supervised learning; text and opinion mining; spatio-temporal and stream data mining; factor and tensor analysis; healthcare, bioinformatics and;

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Big Data Analytics and Knowledge Discovery

mining, text mining, sequences, graph mining, and parallel processing.;

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Data Mining and Knowledge Discovery With Evolutionary Algorithms

knowledge from data. The motivation for applying evolutionary algorithms to data mining is that evolutionary algorithms are robust search methods;

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Advances in Knowledge Discovery and Data Mining

from 458 submissions. They are organized in topical sections named: classification and deep learning; social network and graph mining; privacy;

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Advances in Knowledge Discovery and Data Mining

from 458 submissions. They are organized in topical sections named: classification and deep learning; social network and graph mining; privacy;

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Advances in Knowledge Discovery and Data Mining

; spatiotemporal and image data; anomaly detection and clustering; novel models and algorithms; and text mining and recommender systems.;

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Advances in Knowledge Discovery and Data Mining

; spatiotemporal and image data; anomaly detection and clustering; novel models and algorithms; and text mining and recommender systems.;

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Introduction to Bioinformatics

learning by the student - Guidance to help students answer problems from the text, to support and encourage self-directed learning;

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