Earticle

The interests of web users can be revealed by their visited web pages and time duration on these web pages during their surfing. Time duration on a web page is characterized as a fuzzy linguistic variable because linguistic variable makes users easily understand the expression of time duration and can disregard subtle difference between two time durations. Each web access pattern from web logs is transformed as corresponding fuzzy web access pattern, which is a fuzzy vector composed of fuzzy linguistic variables or 0. Each element in fuzzy web access patterns represents visited web page and time duration on this web page. This paper proposed a rough k-means clustering algorithm based on properties of rough variable to group the gained fuzzy web access patterns. Finally, an example and experiment is provided to illustrate the clustering process. Using this approach, users can effectively mine web logs records to discover interesting user access patterns.

Computational models of the artificial intelligence such as rough set theory have several applications. Rough set-based data clustering can be considered further as a technique for medical decision making. This paper presents the results of an experimental study of a rough-set based clustering technique using Variable Precision Rough Set (VPRS). Here, we employ our proposed clustering technique [12] through a medical dataset of patients suspected diabetic. Our results indicate that the VPRS-based technique is better than that the standard rough set-based techniques in the process of selecting a clustering attribute.

The paper firstly describes some typical manual and automatic semantic annotation, then analyses strategy of semantic annotation based on ontology. Secondly, this paper proposes semantic hierarchical structure of fuzzy ontology based on Variable Precision Rough Set and concept lattice in order to make up the shortage of traditional semantic annotation methods. Novel algorithm of semantic annotation based on fuzzy ontology for domain Webpage is presented in the paper by comparing ontology data of extractive webpage. Finally, experiments show that this presented algorithm is better than the traditional method in semantic annotation rate of support and recall.

Rough K-means algorithm has shown that it can provides a reasonable set of lower and upper bounds for a given dataset. With the conceptions of the lower and upper approximate sets, rough k-means clustering and its emerging derivatives become valid algorithms in vague information clustering. However, the most available algorithms ignore the difference of the distances between data objects and cluster centers when computing new mean for each cluster. To solve this issue, an improved algorithm of rough k-means clustering based on variable weighted distance measure is presented in this article. Comparative experimental results of real world data from UCI demonstrate the validity of the proposed algorithm.