This document discusses RDIET, a framework for text mining that recognizes and discovers information from extracted text. It presents an input-output model for text mining and discusses literature on text mining techniques. The problem statement introduces RDIET for demonstrating a text mining framework. The architecture of RDIET includes information retrieval, pre-processing, selection, refinement, and knowledge extraction. Screenshots are also provided.

1. 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

2. Outline
 Introduction
 I/O Model for Text Mining
 Literature Survey
 Problem Statement
 Architecture Diagram
 Filtering Process
 Screenshots
 References
CONTENTS
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3. 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 data
Data Mining
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4. 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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5. Input-Output Model for Text Mining
Input
Text Mining
Technique
Output
Patterns
Connections
Trends
Documents
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6. Literature Survey
Dramatic growth rate of digital data
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7. 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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8. 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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9. RDIET Architecture
Specification
Information
Retrieval
Pre-
Processing
Selection
Refinement
Knowledge
Extraction
Background
Knowledge
DB DB
.pdf
.docx
.txt
Documents
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10. Filtering Process
.pdf
.doc
.jpg
.docx
.txt
.doc
.pdf
.html
.mpeg
.html
.mp4
.docx
….
Filtering
Process
Documents Browsed from System Screened Documents
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11. Screenshots
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12. Screenshots Cont.
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13. Screenshots Cont.
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14. 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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15. 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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