N
Norwati Mustapha
Researcher at Universiti Putra Malaysia
Publications - 210
Citations - 2025
Norwati Mustapha is an academic researcher from Universiti Putra Malaysia. The author has contributed to research in topics: Cluster analysis & Web mining. The author has an hindex of 20, co-authored 203 publications receiving 1773 citations. Previous affiliations of Norwati Mustapha include Information Technology University.
Papers
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Journal Article
A Survey: Clustering Ensembles Techniques
TL;DR: The clustering ensembles combine multiple partitions generated by different clustering algorithms into a single clustering solution, representation of multiple partitions, its challenges and present taxonomy of combination algorithms.
Journal ArticleDOI
WebPUM: A Web-based recommendation system to predict user future movements
TL;DR: A recommendation system called WebPUM is developed, an online prediction using Web usage mining system and a novel approach for classifying user navigation patterns to predict users' future intentions is proposed, based on the new graph partitioning algorithm.
Posted Content
Data Stream Clustering: Challenges and Issues
TL;DR: This survey tries to clarify, first, the different problem definitions related to data stream clustering in general; second, the specific difficulties encountered in this field of research; third, the varying assumptions, heuristics, and intuitions forming the basis of different approaches.
Journal ArticleDOI
Data stream clustering by divide and conquer approach based on vector model
TL;DR: DCSTREAM method is proposed with regard to the issues to cluster big datasets using the vector model and k-Means divide and conquer approach and Experimental results show that DCSTREAM can achieve superior quality and performance as compare to STREAM and ConStream methods for abrupt and gradual real world datasets.
Proceedings ArticleDOI
A Web Usage Mining Approach Based on LCS Algorithm in Online Predicting Recommendation Systems
TL;DR: An architecture for online predicting in Web Usage Mining system is advanced and a novel approach based on LCS algorithm for classifying user navigation patterns for predicting users' future requests is proposed, which can improve accuracy of classification in the architecture.