Algorithms for clustering data
Algorithms for clustering data
Machine Learning - Special issue on learning with probabilistic representations
Machine Learning
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Semi-supervised Clustering by Seeding
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Constraint-based clustering in large databases
ICDT '01 Proceedings of the 8th International Conference on Database Theory
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Conceptual Design of Data Warehouses from E/R Schema
HICSS '98 Proceedings of the Thirty-First Annual Hawaii International Conference on System Sciences-Volume 7 - Volume 7
A personalization framework for OLAP queries
Proceedings of the 8th ACM international workshop on Data warehousing and OLAP
HISSCLU: a hierarchical density-based method for semi-supervised clustering
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
User Oriented Hierarchical Information Organization and Retrieval
ECML '07 Proceedings of the 18th European conference on Machine Learning
A framework for recommending OLAP queries
Proceedings of the ACM 11th international workshop on Data warehousing and OLAP
Proceedings of the 2008 ACM conference on Computer supported cooperative work
An active learning framework for semi-supervised document clustering with language modeling
Data & Knowledge Engineering
Discovering and using groups to improve personalized search
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Data Mining and Knowledge Discovery
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Fuzzy clustering with viewpoints
IEEE Transactions on Fuzzy Systems
New Frontiers in business intelligence: distribution and personalization
ADBIS'10 Proceedings of the 14th east European conference on Advances in databases and information systems
Which clustering do you want? inducing your ideal clustering with minimal feedback
Journal of Artificial Intelligence Research
Fuzzy transforms method in prediction data analysis
Fuzzy Sets and Systems
Mining preferences from OLAP query logs for proactive personalization
ADBIS'11 Proceedings of the 15th international conference on Advances in databases and information systems
On the effects of constraints in semi-supervised hierarchical clustering
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
Hierarchical confidence-based active clustering
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Group topic modeling for academic knowledge discovery
Applied Intelligence
SHACUN: semi-supervised hierarchical active clustering based on ranking constraints
ICDM'12 Proceedings of the 12th Industrial conference on Advances in Data Mining: applications and theoretical aspects
Performing groupization in data warehouses: which discriminating criterion to select?
NLDB'12 Proceedings of the 17th international conference on Applications of Natural Language Processing and Information Systems
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The groupization aims to enrich the individual preferences using similar individual's data. It may efficiently adapt the query results to the user expectations. In this paper, we aim to optimally identify the analyst' groups in a data warehouse. For that reason, we study the similarity between the selected queries in the analytical history. To enhance the quality of derived groups of analysts, we introduce a new method of semi-supervised hierarchical clustering under constraints ranking for handling cases when some constraints are more important than others and must be firstly enforced during the groupization process. Four axis for group identification are distinguished: (i)聽the function exerted, (ii)聽the granted responsibilities to accomplish goals, (iii)聽the source of groups identification, (iv)聽the dynamicity of discovered groups. Carried out experiments on real log files used for decision-maker groupization in data warehouse confirm the soundness of our approach. Our findings demonstrate that groupization improves upon personalization for several group types, mainly for function-based groupization and explicitly identified groups.