A dynamic cluster maintenance system for information retrieval
SIGIR '87 Proceedings of the 10th annual international ACM SIGIR conference on Research and development in information retrieval
Algorithms for clustering data
Algorithms for clustering data
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
On relational data versions of c-means algorithms
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
From user access patterns to dynamic hypertext linking
Proceedings of the fifth international World Wide Web conference on Computer networks and ISDN systems
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Automatic personalization based on Web usage mining
Communications of the ACM
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Web user clustering from access log using belief function
Proceedings of the 1st international conference on Knowledge capture
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Information Retrieval
Incremental Clustering for Mining in a Data Warehousing Environment
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
A Generalization-Based Approach to Clustering of Web Usage Sessions
WEBKDD '99 Revised Papers from the International Workshop on Web Usage Analysis and User Profiling
A Cube Model and Cluster Analysis for Web Access Sessions
WEBKDD '01 Revised Papers from the Third International Workshop on Mining Web Log Data Across All Customers Touch Points
A Framework for Efficient and Anonymous Web Usage Mining Based on Client-Side Tracking
WEBKDD '01 Revised Papers from the Third International Workshop on Mining Web Log Data Across All Customers Touch Points
Incremental Clustering and Dynamic Information Retrieval
SIAM Journal on Computing
An Online Recommender System for Large Web Sites
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Relational mountain (density) clustering method and web log analysis
International Journal of Intelligent Systems
A new fuzzy relational clustering algorithm based on the fuzzy C-means algorithm
Soft Computing - A Fusion of Foundations, Methodologies and Applications
An Efficient Technique for Mining Usage Profiles Using Relational Fuzzy Subtractive Clustering
WIRI '05 Proceedings of the International Workshop on Challenges in Web Information Retrieval and Integration
Unsupervised clustering on dynamic databases
Pattern Recognition Letters
Collaborative filtering by personality diagnosis: a hybrid memory- and model-based approach
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
ARS: web page recommendation system for anonymous users based on web usage mining
ECS'10/ECCTD'10/ECCOM'10/ECCS'10 Proceedings of the European conference of systems, and European conference of circuits technology and devices, and European conference of communications, and European conference on Computer science
Guest editorial: special issue on a decade of mining the Web
Data Mining and Knowledge Discovery
| Hi-index | 0.00 |
Web usage models and profiles capture significant interests and trends from past accesses. They are used to improve user experience, say through recommendation of pages, pre-fetching of pages, etc. While browsing behavior changes dynamically over time, many web usage modeling techniques are static due to prohibitive model compilation times and also lack of fast incremental update mechanism. However, profiles have to be maintained so that they dynamically adapt to new interests and trends, since otherwise their use can lead to poor, irrelevant, and mis-targeted recommendations in personalization systems. We present a new profile maintenance scheme, which extends the Relational Fuzzy Subtractive Clustering (RFSC) technique and enables efficient incremental update of usage profiles. An impact factor is defined whose value can be used to decide the need for recompilation. The results from extensive experiments on a large real dataset of web logs show that the proposed maintenance technique, with considerably reduced computational costs, is almost as good as complete remodeling.