Algorithms for clustering data
Algorithms for clustering data
Approximation algorithms for facility location problems (extended abstract)
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Greedy strikes back: improved facility location algorithms
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Introduction to Algorithms: A Creative Approach
Introduction to Algorithms: A Creative Approach
Clustering Algorithms
Spatial Clustering in the Presence of Obstacles
Proceedings of the 17th International Conference on Data Engineering
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Clustering high dimensional data: A graph-based relaxed optimization approach
Information Sciences: an International Journal
Data gravitation based classification
Information Sciences: an International Journal
A novel clustering algorithm based on gravity and cluster merging
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
A DGC-based data classification method used for abnormal network intrusion detection
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Transfer learning for cross-company software defect prediction
Information and Software Technology
Gravitation based classification
Information Sciences: an International Journal
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In this paper we examine recent work in the area of spatial clustering with obstacles [14, 13] and present a discussion of several identified drawbacks. We propose an algorithm, called GRAVIclust, which addresses the identified problems. The algorithm uses a heuristic to pick the initial cluster centres and utilises centre of cluster gravity calculations in order to arrive at the optimal clustering solution. We show that the proposed algorithm not only calculates better initial cluster centres than those calculated in [13] but also that with each iteration of the algorithm we get closer to the optimal clustering solution (as indicated by the converging distance function) as opposed to the randomised results offered by [13].