Theory and Applications for Advanced Text Mining PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Theory and Applications for Advanced Text Mining PDF full book. Access full book title Theory and Applications for Advanced Text Mining by Berko Arendse. Download full books in PDF and EPUB format.
Author: Berko Arendse
Publisher:
ISBN: 9781681173047
Category :
Languages : en
Pages : 300
Get Book Here
Book Description
Due to the growth of computer technologies and web technologies, we can easily collect and store large amounts of text data. We can believe that the data include useful knowledge. Text mining, also referred to as text data mining, roughly equivalent to text analytics, refers to the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. The purpose of Text Mining is to process unstructured information, extract meaningful numeric indices from the text, and, thus, make the information contained in the text accessible to the various data mining algorithms. Information can be extracted to derive summaries for the words contained in the documents or to compute summaries for the documents based on the words contained in them. Hence, you can analyze words, clusters of words used in documents, etc., or you could analyze documents and determine similarities between them or how they are related to other variables of interest in the data mining project. Text mining can help an organization derive potentially valuable business insights from text-based content such as word documents, email and postings on social media streams like Facebook, Twitter and LinkedIn. Mining unstructured data with natural language processing (NLP), statistical modeling and machine learning techniques can be challenging, however, because natural language text is often inconsistent. It contains ambiguities caused by inconsistent syntax and semantics, including slang, language specific to vertical industries and age groups, double entendres and sarcasm. Unstructured text is very common, and in fact may represent the majority of information available to a particular research or data mining project. Even if many important techniques have been developed, the text mining research field continues to expand for the needs arising from various application fields. Text mining techniques have been studied aggressively in order to extract the knowledge from the data since late 1990s. This book highlights the theory and applications of advanced text mining techniques..
Author: Berko Arendse
Publisher:
ISBN: 9781681173047
Category :
Languages : en
Pages : 300
Get Book Here
Book Description
Due to the growth of computer technologies and web technologies, we can easily collect and store large amounts of text data. We can believe that the data include useful knowledge. Text mining, also referred to as text data mining, roughly equivalent to text analytics, refers to the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. The purpose of Text Mining is to process unstructured information, extract meaningful numeric indices from the text, and, thus, make the information contained in the text accessible to the various data mining algorithms. Information can be extracted to derive summaries for the words contained in the documents or to compute summaries for the documents based on the words contained in them. Hence, you can analyze words, clusters of words used in documents, etc., or you could analyze documents and determine similarities between them or how they are related to other variables of interest in the data mining project. Text mining can help an organization derive potentially valuable business insights from text-based content such as word documents, email and postings on social media streams like Facebook, Twitter and LinkedIn. Mining unstructured data with natural language processing (NLP), statistical modeling and machine learning techniques can be challenging, however, because natural language text is often inconsistent. It contains ambiguities caused by inconsistent syntax and semantics, including slang, language specific to vertical industries and age groups, double entendres and sarcasm. Unstructured text is very common, and in fact may represent the majority of information available to a particular research or data mining project. Even if many important techniques have been developed, the text mining research field continues to expand for the needs arising from various application fields. Text mining techniques have been studied aggressively in order to extract the knowledge from the data since late 1990s. This book highlights the theory and applications of advanced text mining techniques..
Author: Shigeaki Sakurai
Publisher:
ISBN: 9789535157007
Category :
Languages : en
Pages : 228
Get Book Here
Book Description
Due to the growth of computer technologies and web technologies, we can easily collect and store large amounts of text data. We can believe that the data include useful knowledge. Text mining techniques have been studied aggressively in order to extract the knowledge from the data since late 1990s. Even if many important techniques have been developed, the text mining research field continues to expand for the needs arising from various application fields. This book is composed of 9 chapters introducing advanced text mining techniques. They are various techniques from relation extraction to under or less resourced language. I believe that this book will give new knowledge in the text mining field and help many readers open their new research fields.
Author: Shigeaki Sakurai
Publisher: BoD – Books on Demand
ISBN: 9535108522
Category : Computers
Languages : en
Pages : 230
Get Book Here
Book Description
Due to the growth of computer technologies and web technologies, we can easily collect and store large amounts of text data. We can believe that the data include useful knowledge. Text mining techniques have been studied aggressively in order to extract the knowledge from the data since late 1990s. Even if many important techniques have been developed, the text mining research field continues to expand for the needs arising from various application fields. This book is composed of 9 chapters introducing advanced text mining techniques. They are various techniques from relation extraction to under or less resourced language. I believe that this book will give new knowledge in the text mining field and help many readers open their new research fields.
Author: Michael W. Berry
Publisher: John Wiley & Sons
ISBN: 9780470689653
Category : Mathematics
Languages : en
Pages : 222
Get Book Here
Book Description
Text Mining: Applications and Theory presents the state-of-the-art algorithms for text mining from both the academic and industrial perspectives. The contributors span several countries and scientific domains: universities, industrial corporations, and government laboratories, and demonstrate the use of techniques from machine learning, knowledge discovery, natural language processing and information retrieval to design computational models for automated text analysis and mining. This volume demonstrates how advancements in the fields of applied mathematics, computer science, machine learning, and natural language processing can collectively capture, classify, and interpret words and their contexts. As suggested in the preface, text mining is needed when “words are not enough.” This book: Provides state-of-the-art algorithms and techniques for critical tasks in text mining applications, such as clustering, classification, anomaly and trend detection, and stream analysis. Presents a survey of text visualization techniques and looks at the multilingual text classification problem. Discusses the issue of cybercrime associated with chatrooms. Features advances in visual analytics and machine learning along with illustrative examples. Is accompanied by a supporting website featuring datasets. Applied mathematicians, statisticians, practitioners and students in computer science, bioinformatics and engineering will find this book extremely useful.
Author: Ashok N. Srivastava
Publisher: CRC Press
ISBN: 1420059459
Category : Business & Economics
Languages : en
Pages : 330
Get Book Here
Book Description
The Definitive Resource on Text Mining Theory and Applications from Foremost Researchers in the FieldGiving a broad perspective of the field from numerous vantage points, Text Mining: Classification, Clustering, and Applications focuses on statistical methods for text mining and analysis. It examines methods to automatically cluster and classify te
Author: Yong Shi
Publisher: Springer Science & Business Media
ISBN: 0857295047
Category : Computers
Languages : en
Pages : 314
Get Book Here
Book Description
Optimization techniques have been widely adopted to implement various data mining algorithms. In addition to well-known Support Vector Machines (SVMs) (which are based on quadratic programming), different versions of Multiple Criteria Programming (MCP) have been extensively used in data separations. Since optimization based data mining methods differ from statistics, decision tree induction, and neural networks, their theoretical inspiration has attracted many researchers who are interested in algorithm development of data mining. Optimization based Data Mining: Theory and Applications, mainly focuses on MCP and SVM especially their recent theoretical progress and real-life applications in various fields. These include finance, web services, bio-informatics and petroleum engineering, which has triggered the interest of practitioners who look for new methods to improve the results of data mining for knowledge discovery. Most of the material in this book is directly from the research and application activities that the authors’ research group has conducted over the last ten years. Aimed at practitioners and graduates who have a fundamental knowledge in data mining, it demonstrates the basic concepts and foundations on how to use optimization techniques to deal with data mining problems.
Author: Ronen Feldman
Publisher: Cambridge University Press
ISBN: 0521836573
Category : Computers
Languages : en
Pages : 423
Get Book Here
Book Description
Publisher description
Author: Gary D. Miner
Publisher: Academic Press
ISBN: 0123870119
Category : Mathematics
Languages : en
Pages : 1095
Get Book Here
Book Description
Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications brings together all the information, tools and methods a professional will need to efficiently use text mining applications and statistical analysis. Winner of a 2012 PROSE Award in Computing and Information Sciences from the Association of American Publishers, this book presents a comprehensive how-to reference that shows the user how to conduct text mining and statistically analyze results. In addition to providing an in-depth examination of core text mining and link detection tools, methods and operations, the book examines advanced preprocessing techniques, knowledge representation considerations, and visualization approaches. Finally, the book explores current real-world, mission-critical applications of text mining and link detection using real world example tutorials in such varied fields as corporate, finance, business intelligence, genomics research, and counterterrorism activities. The world contains an unimaginably vast amount of digital information which is getting ever vaster ever more rapidly. This makes it possible to do many things that previously could not be done: spot business trends, prevent diseases, combat crime and so on. Managed well, the textual data can be used to unlock new sources of economic value, provide fresh insights into science and hold governments to account. As the Internet expands and our natural capacity to process the unstructured text that it contains diminishes, the value of text mining for information retrieval and search will increase dramatically. - Extensive case studies, most in a tutorial format, allow the reader to 'click through' the example using a software program, thus learning to conduct text mining analyses in the most rapid manner of learning possible - Numerous examples, tutorials, power points and datasets available via companion website on Elsevierdirect.com - Glossary of text mining terms provided in the appendix
Author: Mick Benson
Publisher:
ISBN: 9781632400284
Category : Text processing (Computer science)
Languages : en
Pages : 0
Get Book Here
Book Description
This book discusses the topic of advanced text mining and applied principles in detail. Due to the development of computer and web technologies, we are now able to easily compile and store huge amounts of text data. It is believed that data consists of valuable knowledge. Text mining techniques have been dynamically analyzed for the extraction of knowledge from data since late 1990s. Even though many vital methods have been formulated, the text mining research field continues to expand for the needs emerging from distinct application fields. This book provides an introduction of advanced text mining techniques ranging from relation extraction to under or less resourced language. It aims at providing the readers with novel information about the field and assists them in exploring new research fields.
Author: Suman Saha
Publisher: CRC Press
ISBN: 0429949855
Category : Computers
Languages : en
Pages : 261
Get Book Here
Book Description
Advanced data structures is a core course in Computer Science which most graduate program in Computer Science, Computer Science and Engineering, and other allied engineering disciplines, offer during the first year or first semester of the curriculum. The objective of this course is to enable students to have the much-needed foundation for advanced technical skill, leading to better problem-solving in their respective disciplines. Although the course is running in almost all the technical universities for decades, major changes in the syllabus have been observed due to the recent paradigm shift of computation which is more focused on huge data and internet-based technologies. Majority of the institute has been redefined their course content of advanced data structure to fit the current need and course material heavily relies on research papers because of nonavailability of the redefined text book advanced data structure. To the best of our knowledge well-known textbook on advanced data structure provides only partial coverage of the syllabus. The book offers comprehensive coverage of the most essential topics, including: Part I details advancements on basic data structures, viz., cuckoo hashing, skip list, tango tree and Fibonacci heaps and index files. Part II details data structures of different evolving data domains like special data structures, temporal data structures, external memory data structures, distributed and streaming data structures. Part III elucidates the applications of these data structures on different areas of computer science viz, network, www, DBMS, cryptography, graphics to name a few. The concepts and techniques behind each data structure and their applications have been explained. Every chapter includes a variety of Illustrative Problems pertaining to the data structure(s) detailed, a summary of the technical content of the chapter and a list of Review Questions, to reinforce the comprehension of the concepts. The book could be used both as an introductory or an advanced-level textbook for the advanced undergraduate, graduate and research programmes which offer advanced data structures as a core or an elective course. While the book is primarily meant to serve as a course material for use in the classroom, it could be used as a starting point for the beginner researcher of a specific domain.
