Algorithms for clustering data
Algorithms for clustering data
Applied multivariate statistical analysis
Applied multivariate statistical analysis
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Spatial Clustering in the Presence of Obstacles
Proceedings of the 17th International Conference on Data Engineering
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 when Facing Physical Constraints
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
A divisive information theoretic feature clustering algorithm for text classification
The Journal of Machine Learning Research
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Density-based spatial clustering in the presence of obstacles and facilitators
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
ICML '06 Proceedings of the 23rd international conference on Machine learning
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Fast and exact out-of-core and distributed k-means clustering
Knowledge and Information Systems
Optimizing the size and locations of facilities in competitive multi-site service systems
Computers and Operations Research
A neural model for the p-median problem
Computers and Operations Research
Top 10 algorithms in data mining
Knowledge and Information Systems
SAIL: summation-based incremental learning for information-theoretic clustering
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A Generalization of Proximity Functions for K-Means
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Clustering based on matrix approximation: a unifying view
Knowledge and Information Systems
A hybrid spatial data clustering method for site selection: The data driven approach of GIS mining
Expert Systems with Applications: An International Journal
Global IT and IT-enabled services
Information Systems Frontiers
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Given its importance, the problem of selecting the right site for a service entity has attracted great attention in the literature. However, due to its complexity, the quantification of the interrelationships between the service site and its nearby business types is still a challenging task. To this end, in this paper, we propose a novel joint learning scheme for service site selection. This scheme employs both the Probabilistic Latent Semantic Analysis (PLSA) on the Geographical Information System (GIS) data and the partitional clustering on the service performance data. A case study for bank branch selection is provided to demonstrate the usefulness of our method. Finally, based on the joint learning scheme, we present a conceptual framework for the complete procedure of service site selection with a particular emphasis on the GIS enabled network analysis.