The role of domain knowledge in data mining
CIKM '95 Proceedings of the fourth international conference on Information and knowledge management
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Multidimensional binary search trees used for associative searching
Communications of the ACM
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
Clustering with Instance-level Constraints
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
The Role of Domain Knowledge in a Large Scale Data Mining Project
SETN '02 Proceedings of the Second Hellenic Conference on AI: Methods and Applications of Artificial Intelligence
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Clustering Spatial Data in the Presence of Obstacles: a Density-Based Approach
IDEAS '02 Proceedings of the 2002 International Symposium on Database Engineering & Applications
Clustering Spatial Data when Facing Physical Constraints
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Uncertainty Handling and Quality Assesment in Data Mining
Uncertainty Handling and Quality Assesment in Data Mining
Intelligent clustering with instance-level constraints
Intelligent clustering with instance-level constraints
Adaptive duplicate detection using learnable string similarity measures
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Integrating constraints and metric learning in semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A Framework for Semi-Supervised Learning Based on Subjective and Objective Clustering Criteria
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Efficient incremental constrained clustering
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
C-DBSCAN: Density-Based Clustering with Constraints
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
C-DenStream: Using Domain Knowledge on a Data Stream
DS '09 Proceedings of the 12th International Conference on Discovery Science
Measuring constraint-set utility for partitional clustering algorithms
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Agglomerative hierarchical clustering with constraints: theoretical and empirical results
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Graph-based clustering with constraints
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Semi-supervised clustering with discriminative random fields
Pattern Recognition
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Semi-supervised clustering methods guide the data partitioning and grouping process by exploiting background knowledge, among else in the form of constraints. In this study, we propose a semi-supervised density-based clustering method. Density-based algorithms are traditionally used in applications, where the anticipated groups are expected to assume non-spherical shapes and/or differ in cardinality or density. Many such applications, among else those on GIS, lend themselves to constraint-based clustering, because there is a priori knowledge on the group membership of some records. In fact, constraints might be the only way to prevent the formation of clusters that do not conform to the applications' semantics. For example, geographical objects, e.g. houses, separated by a borderline or a river may not be assigned to the same cluster, independently of their physical proximity. We first provide an overview of constraint-based clustering for different families of clustering algorithms. Then, we concentrate on the density-based algorithms' family and select the algorithm DBSCAN, which we enhance with Must-Link and Cannot-Link constraints. Our enhancement is seamless: we allow DBSCAN to build temporary clusters, which we then split or merge according to the constraints. Our experiments on synthetic and real datasets show that our approach improves the performance of the algorithm.