New data structures for orthogonal range queries
SIAM Journal on Computing
Identifying aggregates in hypertext structures
HYPERTEXT '91 Proceedings of the third annual ACM conference on Hypertext
Information retrieval
Cluster analysis for hypertext systems
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Silk from a sow's ear: extracting usable structures from the Web
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Clustering hypertext with applications to web searching
HYPERTEXT '00 Proceedings of the eleventh ACM on Hypertext and hypermedia
Personalization on the Net using Web mining: introduction
Communications of the ACM
Designing Data-Intensive Web Applications
Designing Data-Intensive Web Applications
E-Commerce User Experience
The new k-windows algorithm for improving the k-means clustering algorithm
Journal of Complexity
Mining the Web: Discovering Knowledge from HyperText Data
Mining the Web: Discovering Knowledge from HyperText Data
Improving the Orthogonal Range Search k -Windows Algorithm
ICTAI '02 Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence
Web Usage Mining as a Tool for Personalization: A Survey
User Modeling and User-Adapted Interaction
Semantic resource management for the web: an e-learning application
Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
A Computational Geometry Approach to Web Personalization
CEC '04 Proceedings of the IEEE International Conference on E-Commerce Technology
Adaptivity, personalization, and the semantic web
Proceedings of the joint international workshop on Adaptivity, personalization & the semantic web
An evolutionary data clustering algorithm
ICCOMP'07 Proceedings of the 11th WSEAS International Conference on Computers
Integrating recommendation models for improved web page prediction accuracy
ACSC '08 Proceedings of the thirty-first Australasian conference on Computer science - Volume 74
A survey of Web clustering engines
ACM Computing Surveys (CSUR)
An integrated model for next page access prediction
International Journal of Knowledge and Web Intelligence
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The paper introduces an algorithm for personalized clustering based on a range tree structure, used for identifying all web documents satisfying a set of predefined personal user preferences. The returned documents go through a clustering phase before reaching the end user, thus allowing more effective manipulation and supporting the decision making process. The proposed algorithm demonstrates increased applicability in semantic web settings, since they offer the infrastructure for the explicit declaration of web document attributes and their respective values, thus allowing for more automated retrieval. The proposed algorithm improves the k-means range algorithm, as it uses the already constructed range tree (i.e. during the personalized filtering phase) as the basic structure on which the clustering step is based, applying instead of the k-means, the k-windows algorithm. The total number of parameters used for modeling the web documents dictates the number of dimensions of the Euclidean space representation. The time complexity of the algorithm is O(logd-2n+v), where d is the number of dimensions, n is the total number of web documents and v is the size of the answer.