Algorithms for clustering data
Algorithms for clustering data
A divisive scheme for constructing minimal spanning trees in coordinate space
Pattern Recognition Letters
An efficient agglomerative clustering algorithm using a heap
Pattern Recognition
The SEQUOIA 2000 storage benchmark
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
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
Web document clustering: a feasibility demonstration
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
ACM Computing Surveys (CSUR)
Fast hierarchical clustering and other applications of dynamic closest pairs
Journal of Experimental Algorithmics (JEA)
Data mining: concepts and techniques
Data mining: concepts and techniques
A Distribution-Based Clustering Algorithm for Mining in Large Spatial Databases
ICDE '98 Proceedings of the Fourteenth 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
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
An Incremental Hierarchical Data Clustering Algorithm Based on Gravity Theory
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Connecting the dots: mass, energy, word meaning, and particle-wave duality
QI'12 Proceedings of the 6th international conference on Quantum Interaction
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This paper discusses the clustering quality and complexities of the hierarchical data clustering algorithm based on gravity theory. The gravity-based clustering algorithm simulates how the given N nodes in a K-dimensional continuous vector space will cluster due to the gravity force, provided that each node is associated with a mass. One of the main issues studied in this paper is how the order of the distance term in the denominator of the gravity force formula impacts clustering quality. The study reveals that, among the hierarchical clustering algorithms invoked for comparison, only the gravity-based algorithm with a high order of the distance term neither has a bias towards spherical clusters nor suffers the well-known chaining effect. Since bias towards spherical clusters and the chaining effect are two major problems with respect to clustering quality, eliminating both implies that high clustering quality is achieved. As far as time complexity and space complexity are concerned, the gravity-based algorithm enjoys either lower time complexity or lower space complexity, when compared with the most well-known hierarchical data clustering algorithms except single-link.