Text mining documents in electronic data interchange environment

Authors:
Zakaria Suliman Zubi
Affiliations:
Sirt University, Faculty of Science, Computer Science Department, Sirte, Libya
Venue:
NN'10/EC'10/FS'10 Proceedings of the 11th WSEAS international conference on nural networks and 11th WSEAS international conference on evolutionary computing and 11th WSEAS international conference on Fuzzy systems
Year:
2010

Citing 9
Cited 0

Automatic text processing

Automatic text processing
An effective document clustering method using user-adaptable distance metrics

Proceedings of the 2002 ACM symposium on Applied computing
Case Studies in Information and Computer Ethics

Case Studies in Information and Computer Ethics
Modeling the Clickstream: Implications for Web-Based Advertising Efforts

Marketing Science
Empirical and Theoretical Comparisons of Selected Criterion Functions for Document Clustering

Machine Learning
Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data

Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data
I-extended databases

MMACTEE'08 Proceedings of the 10th WSEAS International Conference on Mathematical Methods and Computational Techniques in Electrical Engineering
Knowledge discovery query language (KDQL)

ICCOMP'08 Proceedings of the 12th WSEAS international conference on Computers
Using some web content mining techniques for Arabic text classification

DNCOCO'09 Proceedings of the 8th WSEAS international conference on Data networks, communications, computers

Quantified Score

Hi-index	0.00

Visualization

Abstract

The internet is a huge source of documents, containing a massive number of texts presented in multilingual languages on a wide range of topics. These texts are demonstrating in an electronic documents format hosted on the web. The documents exchanged using special forms in an Electronic Data Interchange (EDI) environment. Using web text mining approaches to mine documents in EDI environment could be new challenging guidelines in web text mining. Applying text-mining approaches to discover knowledge previously unknown patters retrieved from the web documents by using partitioned cluster analysis methods such as k- means methods using Euclidean distance measure algorithm for EDI text document datasets is unique area of research these days. Our experiments employ the standard K-means algorithm on EDI text documents dataset that most commonly used in electronic interchange. We also report some results using text mining clustering application solution called WEKA. This study will provide high quality services to any organization that is willing to use the system.