Data Mining - Concepts and Techniques.

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. This comprehensive professional reference brings together all the information, tools and methods a professional will need to efficiently use text mining applications and statistical analysis. The Handbook of Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications 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.-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

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Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications

Text Mining constitutes the process of knowledge discovery, previously unknown and potentially useful, though the automatic information extraction from texts...

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Modelo de representación de textos basado en grafo para la minería de texto

Eight short surveys based on select items from the National Survey of Student Engagement (NSSE) were administered to approximately five hundred students over a nine-week period at five diverse colleges and universities. The goal of the experiment was to investigate what impact a survey panel data collection approach would have on recruitment, survey data quality indicators, and scale properties. Results indicated higher response rates, shorter survey duration, and minimal impact on scale factor structures. However, both cost of incentives and panel member attrition make this alternative survey method less attractive than it would be otherwise.

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An Alternative Approach: Using Panels to Survey College Students. Professional File. Article 138, Fall 2016.

Text mining presents an efficient means to access the comprehensive amount of data found in written records by converting words into numbers and using algorithms to detect relevant patterns. This article presents the fundamentals of text mining, including an overview of key concepts, prevalent methodologies in this work, and popular software packages. The utility of text mining is demonstrated through description of two promising practices and presentation of two detailed examples. The two promising practices are (1) using text analytics to understand and minimize course withdrawals, and (2) assessing student understanding and depth of learning in science, technology, engineering and mathematics (STEM) (physics). The two detailed examples are (1) refining survey items on the National Survey of Student Engagement (NSSE), and (2) using text to create a learning analytics system at a community college (City University of New York [CUNY]: the Stella and Charles Guttman Community College, or CUNY Guttman). Results of this study include identification of additional item choices for the survey and discovery of a relationship between e-portfolio content and academic performance. Additional examples of text mining in higher education and ethical considerations pertaining to this technology are also discussed. FRAMING THE ISSUE OF TEXT MINING Students generate copious amounts of thick, rich data; however, these data are often unexamined because traditional qualitative methodologies used to examine thousands of submissions require extensive resources. Text mining (the machine coding of text with the goal of integrating converted submissions with quantitative methods) offers timely, accurate, and actionable assistance (Zhang & Segall, 2010). This article presents detailed information on how text mining can be used by staff who work in institutional research (IR) and collect, but often are forced to neglect, text-based data. Examples of text data accessible to IR staff are • Application essays, • Written assignments, • Open-ended survey responses, • Course Management Software (CMS) postings, • Student blogs, • Course evaluations, • Surveys, and • E-portfolios. IR professionals recognize the depth of text data that are—or potentially can be—collected, but might be uncertain how to process those data and use them for campus research informing decision-making. This article presents an overview of the concepts and strategies of text mining and text analytics, and acquaints the reader with the terminology, methodology, and software associated with this technology. Two examples using text data in higher education illustrate how mining can be carried out. It is hoped that the reader will develop a fundamental understanding of text mining, be able to suggest how it can be used to aid decision-makers, and understand the advantages as well as the constraints of this approach to data analysis.

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Mining Text Data: Making Sense of What Students Tell Us. Professional File. Article 139, Fall 2016.

Text Data Mining or Knowledge-Discovery in Text (KDT) technique refers generally to the process of extracting interesting and non-trivial information and knowledge from unstructured text. Text mining technique is a deviation on a countryside called data mining that tries to find interesting patterns from large databases; text mining also known as the Intelligent Text Analysis (ITA). Text mining is a young interdisciplinary field which draws on information retrieval, data mining, machine learning, statistics and computational linguistics. Text Mining Technique (TMT) is the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources. Text mining, sometimes alternately 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. Text mining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. 'High quality' in text mining usually refers to some combination of relevance, novelty, and interestingness. In this paper, we introduce the rising of Text Mining Technique as unforeseen-part of the Data Mining and Data Warehouse Methodologies; for improving its role, performances and productivities and also used in different research areas.

http://ijarcsee.org/index.php/IJARCSEE/article/download/80/74

Rising of Text Mining Technique: As Unforeseen-part of Data Mining

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Jitendra Raghuvanshi

Papers

Rising of Text Mining Technique: As Unforeseen-part of Data Mining

Elevate and Forward Motion Approach-Path in Construction of Data Warehouse