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journal of
intelligent systems |
SPECIAL ISSUE: web-based information
retrieval
Guest Editor: Bhanu Prasad
Welcome to the special issue on Web-Based Information Retrieval of the Journal of Intelligent Systems (JIS). Information retrieval has been a popular area of computer and information sciences. One can imagine the difficulty of getting relevant information from a system, without having a suitable retrieving technique. Due to the tremendous growth and popularity of the World Wide Web, lot of information is made available over the web. As a result, retrieving this information became an important problem. Several technologies, ranging from simple XML scripts to complex ontologies, are developed to address this issue. The goal of this peer reviewed special issue is to present and share the current technology in the theory and application areas of web-based information retrieval, which is a fruitful combination of information retrieval and web-based technologies.
In the first paper, Select Strong Information Features to Improve Text Categorization Effectiveness, by Dejun Xue and Maosong Sun of the Tsinghua University, the authors focused on feature dimensionality issues in text categorization. Text categorization is an important problem in web-based information retrieval. The authors also present a Chinese document classification system, which is based on the class-centroid based classifier and the Chinese character bigram features. Evaluation results are also presented by considering a large amount of text data as the platform.
The next article, An Intelligent Domain Specific Information Retrieval System, is presented by Eugene Kozlov and Bhanu Prasad from the Russian Academy of Sciences and the Florida A & M University. This paper presents a web-based and domain specific information retrieval system. The authors discuss about modeling the user queries and the domain knowledge by using graph theoretic and ontological techniques. A comparative study of the system with keyword-based systems is also presented.
The paper, Unsupervised Characterization of Web Users:
Conventional and Fuzzy Approaches, presented by Pawan Lingras, Rui Yan,
and Adish Jain from the Saint Mary's
University is the next article of the special issue. The authors compare and
contrast web usage mining results obtained by fuzzy C-means clustering
algorithm and conventional K-means clustering algorithm. The authors
also present the advantages of fuzzy
cluster memberships over conventional crisp clusters. A discussion on fuzzy
clustering in e-commerce is also presented.
The last article of the special issue is by Pawan Lingras and Rui Yan of the Saint Mary’s University, and Mofreh Hogo of the Czech Technical University. In their paper, Evolutionary, Neural, and Statistical Approaches to Interval Clustering for Web Mining, the authors provide some solutions for handling the interval representations of clustering operations in data mining technology. The authors use soft computing techniques such as genetic algorithms to demonstrate their work. Rough-set based clustering of web users is also illustrated in this article. Web mining technologies are an integral part of web-based information retrieval.
Bhanu Prasad
Guest Editor
Department of Computer and Information Sciences
Florida A&M University, Tallahassee, FL 32307 USA
e-mail: bhanu.prasad@famu.edu
contents
Select Strong
Information Features to Improve Text Categorization Effectiveness
An Intelligent Domain Specific Information Retrieval
System
Unsupervised
Characterization of Web Users: Conventional and
Fuzzy Approaches,
Evolutionary, Neural, and Statistical Approaches to
Interval
Clustering for Web Mining
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