Results 261 to 270 of about 119,353 (308)

Data mining on text

Proceedings. The Twenty-Second Annual International Computer Software and Applications Conference (Compsac '98) (Cat. No.98CB 36241), 2002
Data mining technology is giving us the ability to extract meaningful patterns from large quantities of structured data. Information retrieval systems have made large quantities of textual data available. Extracting meaningful patterns from this data is difficult. Current tools for mining structured data are inappropriate for free text.
Chris Clifton, Rick Steinheiser
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Text Knowledge Mining: An Alternative to Text Data Mining

2008 IEEE International Conference on Data Mining Workshops, 2008
In this paper we introduced an alternative view of text mining and we review several alternative views proposed by different authors. We propose a classification of text mining techniques into two main groups: techniques based on inductive inference, that we call text data mining (TDM, comprising most of the existing proposals in the literature), and ...
Daniel Sánchez 0001, María J. Martín-Bautista, Ignacio J. Blanco, Consuelo Justicia de la Torre   +3 more
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Data Analysis Support by Combining Data Mining and Text Mining

2017 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI), 2017
In recent years, data mining and text mining techniques have been frequently used for analyzing questionnaire and review data. Data mining techniques such as association analysis and cluster analysis are used for marketing analysis, because those can discover relationships and rules hiding in enormous numerical data.
Tomoya Matsumoto, Wataru Sunayama, Yuji Hatanaka, Kazunori Ogohara   +3 more
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Data mining and text mining — A survey

2017 International Conference on Computation of Power, Energy Information and Commuincation (ICCPEIC), 2017
In this paper we mainly focus on the techniques of data mining such as clustering, classification etc. In today's strategy it becomes a hectic task to gather, analyze and extract huge amount of datasets. So we use many efficient methods for the practical integration of the data.
R. Suresh, S. R. Harshni
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Semantic Text Mining with Linked Data

2009 Fifth International Joint Conference on INC, IMS and IDC, 2009
Linked Data is an open data space that emerges from the publication and interlinking of structured data on the Web using the Semantic Web technologies. How to utilize this wealth of data is currently a focused research theme of the Semantic Web community.
Zhaohui Huang, Huajun Chen, Tong Yu 0003, Hao Sheng 0004, Zhaobo Luo, Yuxin Mao   +5 more
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Text data mining: a case study

International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume II, 2005
A vast amount of data is available on the World Wide Web. Many companies have started to mine this data to augment datasets used in production. This paper presents an industry study for one such text data mining. The study and analysis of the extracted data from the World Wide Web could be used to help improve products and services.
Charles Wesley Ford, Chia-Chu Chiang, Hao Wu, Radhika R. Chilka, John R. Talburt   +4 more
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Legal Literacies for Text Data Mining

2020
"Legal Literacies for Text Data Mining" outlines for digital humanities researchers and professionals some of the core skills and understandings needed to navigate law, policy, ethics, and risk in digital humanities text and data mining (TDM) projects, with reference to common DH use cases.
Stacy Reardon, Rachael Samberg, Glen Worthey   +2 more
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Text Summarization in Data Mining

2002
Text summarizers automatically construct summaries of a natural-language document. This paper examines the use of text summarization within data mining, identifying the potential summarizers have for uncovering interesting and unexpected information.
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Text and data mining of in-copyright works

Communications of the ACM, 2021
How copyright law might be an impediment to text and data mining research.
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Combining Text Mining and Data Mining for Bug Report Classification

2014 IEEE International Conference on Software Maintenance and Evolution, 2014
Misclassification of bug reports inevitably sacrifices the performance of bug prediction models. Manual examinations can help reduce the noise but bring a heavy burden for developers instead. In this paper, we propose a hybrid approach by combining both text mining and data mining techniques of bug report data to automate the prediction process.
Yu Zhou 0010, Yanxiang Tong, Ruihang Gu, Harald C. Gall   +3 more
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