Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Applied multivariate techniques
Applied multivariate techniques
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
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
Data mining: concepts and techniques
Data mining: concepts and techniques
Self-Organizing Maps
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Constraint-based clustering in large databases
ICDT '01 Proceedings of the 8th International Conference on Database Theory
Spatial Clustering in the Presence of Obstacles
Proceedings of the 17th International Conference on Data Engineering
Quality Scheme Assessment in the Clustering Process
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
AUTOCLUST+: Automatic Clustering of Point-Data Sets in the Presence of Obstacles
TSDM '00 Proceedings of the First International Workshop on Temporal, Spatial, and Spatio-Temporal Data Mining-Revised Papers
Clustering Spatial Data in the Presence of Obstacles: a Density-Based Approach
IDEAS '02 Proceedings of the 2002 International Symposium on Database Engineering & Applications
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Dual Clustering: Integrating Data Clustering over Optimization and Constraint Domains
IEEE Transactions on Knowledge and Data Engineering
Adherence clustering: an efficient method for mining market-basket clusters
Information Systems
Constrained data clustering by depth control and progressive constraint relaxation
The VLDB Journal — The International Journal on Very Large Data Bases
Discovering personally meaningful places: An interactive clustering approach
ACM Transactions on Information Systems (TOIS)
Adherence clustering: an efficient method for mining market-basket clusters
Information Systems
Incremental clustering in geography and optimization spaces
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Efficient privacy-aware search over encrypted databases
Proceedings of the 4th ACM conference on Data and application security and privacy
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Clustering is the problem of grouping data based on similarity. While this problem has attracted the attention of many researchers for many years, we are witnessing a resurgence of interest in new clustering techniques. In this paper we discuss some very recent clustering approaches and recount our experience with some of these algorithms. We also present the problem of clustering in the presence of constraints and discuss the issue of clustering validation.