How many clusters are best?—an experiment
Pattern Recognition
The nature of statistical learning theory
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Advances in knowledge discovery and data mining
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Data mining: concepts and techniques
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Pattern Recognition with Fuzzy Objective Function Algorithms
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Introduction to the Theory of Neural Computation
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Polynomial-Time Decomposition Algorithms for Support Vector Machines
Machine Learning
Data Mining: An Overview from a Database Perspective
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Constrained Clustering as an Optimization Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatial Clustering in the Presence of Obstacles
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Indexing the Distance: An Efficient Method to KNN Processing
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On Data Clustering Analysis: Scalability, Constraints, and Validation
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
A robust and efficient clustering algorithm based on cohesion self-merging
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ILP'02 Proceedings of the 12th international conference on Inductive logic programming
The analysis of decomposition methods for support vector machines
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Minimum sum-squared residue for fuzzy co-clustering
Intelligent Data Analysis
A multiview approach for intelligent data analysis based on data operators
Information Sciences: an International Journal
Image-mapped data clustering: An efficient technique for clustering large data sets
Intelligent Data Analysis
Clustering Data Streams in Optimization and Geography Domains
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
A Framework for Multi-Objective Clustering and Its Application to Co-Location Mining
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Integrating Data Clustering and Visualization for the Analysis of 3D Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Incremental clustering in geography and optimization spaces
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Efficient joint clustering algorithms in optimization and geography domains
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Editorial: New fuzzy c-means clustering model based on the data weighted approach
Data & Knowledge Engineering
Spatio-temporal clustering of road network data
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I
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Spatial clustering has attracted a lot of research attention due to its various applications. In most conventional clustering problems, the similarity measurement mainly takes the geometric attributes into consideration. However, in many real applications, the nongeometric attributes are what users are concerned about. In the conventional spatial clustering, the input data set is partitioned into several compact regions and data points which are similar to one another in their nongeometric attributes may be scattered over different regions, thus making the corresponding objective difficult to achieve. To remedy this, we propose and explore in this paper a new clustering problem on two domains, called dual clustering, where one domain refers to the optimization domain and the other refers to the constraint domain. Attributes on the optimization domain are those involved in the optimization of the objective function, while those on the constraint domain specify the application dependent constraints. Our goal is to optimize the objective function in the optimization domain while satisfying the constraint specified in the constraint domain. We devise an efficient and effective algorithm, named Interlaced Clustering-Classification, abbreviated as ICC, to solve this problem. The proposed ICC algorithm combines the information in both domains and iteratively performs a clustering algorithm on the optimization domain and also a classification algorithm on the constraint domain to reach the target clustering effectively. The time and space complexities of the ICC algorithm are formally analyzed. Several experiments are conducted to provide the insights into the dual clustering problem and the proposed algorithm.