Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
Polynomial-Time Decomposition Algorithms for Support Vector Machines
Machine Learning
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Spatial Clustering in the Presence of Obstacles
Proceedings of the 17th International Conference on Data Engineering
C2P: Clustering based on Closest Pairs
Proceedings of the 27th 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
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
WaveCluster: a wavelet-based clustering approach for spatial data in very large databases
The VLDB Journal — The International Journal on Very Large Data Bases
The Journal of Machine Learning Research
Clustering in Dynamic Spatial Databases
Journal of Intelligent Information Systems
Dual Clustering: Integrating Data Clustering over Optimization and Constraint Domains
IEEE Transactions on Knowledge and Data Engineering
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
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
Enhancing search in a geospatial multimedia annotation system
Proceedings of the 12th International Conference on Information Integration and Web-based Applications & Services
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Spatial clustering has been identified as an important technique in data mining owing to its various applications. In the conventional spatial clustering methods, data points are clustered mainly according to their geographic attributes. In real applications, however, the obtained data points consist of not only geographic attributes but also non-geographic ones. In general, geographic attributes indicate the data locations and non-geographic attributes show the characteristics of data points. It is thus infeasible, by using conventional spatial clustering methods, to partition the geographic space such that similar data points are grouped together. In this paper, we propose an effective and efficient algorithm, named incremental clustering toward the Bound INformation of Geography and Optimization spaces, abbreviated as BINGO, to solve the problem. The proposed BINGO algorithm combines the information in both geographic and non-geographic attributes by constructing a summary structure and possesses incremental clustering capability by appropriately adjusting this structure. Furthermore, most parameters in algorithm BINGO are determined automatically so that it is easy to be applied to applications without resorting to extra knowledge. Experiments on synthetic are performed to validate the effectiveness and the efficiency of algorithm BINGO.