text mining framework
TRANSCRIPT
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RDIET: Recognition and Discovery of Information from Extracted TEXT
Prakhyath Rai
III Semester M.Tech. {CSE}
NMAMIT, Nitte
Under the Guidance of:
Mr. Vijay Murari
Asst. Professor, Dept. of CSE
NMAMIT, Nitte
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Outline
Introduction
I/O Model for Text Mining
Literature Survey
Problem Statement
Architecture Diagram
Filtering Process
Screenshots
References
CONTENTS
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Introduction
Text Mining is a Discovery
Text Mining is used to extract relevant information or
knowledge or pattern from different sources that are in
unstructured form
• Handles semi-structured or Unstructured dataText Mining
• Handles structured dataData Mining
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Introduction Cont.
Extract and discover knowledge hidden in text automatically
Aid domain experts by automatically:
identifying concepts
extracting facts/relations
discovering implicit links
generating hypotheses
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Input-Output Model for Text Mining
Input
Text MiningTechnique
Output
Patterns ConnectionsTrends
Documents
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Literature SurveyDramatic growth rate of digital data
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Literature Survey Cont.
Information Extraction (IE)
Knowledge Discovery from Databases (KDD)
KDT (Knowledge Discovery from Text)
High Specificity i.e. Low frequency Problem
Misinterpretations with low frequency pattern
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Problem Statement
RDIET {Recognition and Discovery of Information from Extracted Text} demonstrates a framework for text mining
RDIET
IE
KDT
Standard Rule Induction
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RDIET Architecture
Specification Information
Retrieval
Pre-
Processing Selection
Refinement Knowledge
Extraction
Background
Knowledge
DB DB
.docx
.txt
Documents
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Filtering Process
.doc
.jpg
.docx
.txt
.doc
.html
.mpeg
.html
.mp4
.docx
….
Filtering
Process
Documents Browsed from System Screened Documents
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Screenshots
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Screenshots Cont.
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Screenshots Cont.
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References[1] Ning Zhong, Yuefeng Li and T. Grance, “Effective Pattern Discovery for Text Mining,”
IEEE Transactions on Knowledge and Data Engineering, Vol. 24, No. 1, January 2012.
[2] Sangno Lee, Jeff Baker and Jaeki, “An Emperical Comparison of Four Text Mining
Methods”, Proceedings of the 43rd Hawaii International Conferences on System Sciences,
2010.
[3] Gary King, Patrick Lam and Margaret E Aroberts, “Computer-Assisted Keyword and
Documents from Unstructured Text”, 2014.
[4] Deepak Agnihotri, Kesari Verma and Priyanka Tripathi, “Pattern and Cluster Mining on
Text Data”, Fourth International Conferences on Communication Systems and Network
Technologies, 2014.
[5] Robert Moro and Maria Bielikova, “Personalized Text Summarization Based on
Important Terms Identification”, 23rd International Workshop on Database and Expert
Systems Applications, 2012.
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Reference Cont.[6] M Sukanya and S Biruntha, “Techniques on Text Mining”, IEEE Conference on Advanced Communication Control and Computing Technologies, 2012.
[7] Christina Feilmayr, “Text Mining-Supported Information Extraction”, 22nd
International Workshop on Database and Expert Systems Applications, 2012.
[8] hadoop.intel.com, intel.com/bigdata, intel.com/microservers, “Extract, Transform, and Load Big Data with Apache Hadoop”, http://hadoop.intel.com
[9] Raymond J Mooney and Un Yong Nahm, “ Text Mining with Information Extraction”, Proceedings of the 4th International MIDP Colloquium, pages 141-160, Van Schaik Pub., South Africa, 2005.
[10] R Baeza-Yates and B Ribeiro-Neto. “Modern Information Retrieval”, ACM Press, New York, 1999.
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Depatment of CSE NMAMIT, Nitte