Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
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
Evaluation of hierarchical clustering algorithms for document datasets
Proceedings of the eleventh international conference on Information and knowledge management
Modeling the Clickstream: Implications for Web-Based Advertising Efforts
Marketing Science
Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data
Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data
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
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The internet is a huge source of documents, containing a massive number of texts 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 utilize the standard K-means algorithm on EDI text documents dataset that most commonly used in electronic interchange and we 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.