Authors:
Iraj Mahdavi
1
;
Babak Shirazia
1
;
Namjae Chob
2
;
Navid Sahebjamniaa
1
and
Meysam Aminzadeha
1
Affiliations:
1
Mazandaran University of Science & Technology, Iran, Islamic Republic of
;
2
The School of Business, Hanyang University, Korea, Republic of
Keyword(s):
e-CRM; data mining; Web document clustering; neuro-fuzzy approach.
Related
Ontology
Subjects/Areas/Topics:
Advanced Applications of Fuzzy Logic
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Engineering
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Human-Computer Interaction
;
Information Systems Analysis and Specification
;
Knowledge Management
;
Methodologies and Methods
;
Neural Network Software and Applications
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Ontologies and the Semantic Web
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Society, e-Business and e-Government
;
Soft Computing
;
Theory and Methods
;
Web Information Systems and Technologies
Abstract:
Internet technology enables companies to capture new customers, track their performances and online behavior, and customize communications, products, services, and price. The analysis of customers and customer interactions for electronic customer relationship management (e-CRM) can be performed by data-mining (DM), optimization methods, or combined approaches. Some of web mining techniques include analysis of user access patterns, web document clustering and classification. Most existing methods of classification are based on a model that assumes a fixed-size collection of keywords or key terms with predefined set of categories. We propose a new approach to obtain category-keyword sets with unknown number of categories. On the basis of the training set of Web documents, the approach is used to classify test documents into a set of initial categories. Finally evolutionary rules are applied to these new sets of keywords and training documents to update the category-keyword sets to real
ize dynamic document classification.
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