Efficiency of hierarchic agglomerative clustering using the ICL distributed array processor
Journal of Documentation
Parallel Algorithms for Hierarchical Clustering and Cluster Validity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal Expected-Time Algorithms for Closest Point Problems
ACM Transactions on Mathematical Software (TOMS)
Categorizing Visitors Dynamically by Fast and Robust Clustering of Access Logs
WI '01 Proceedings of the First Asia-Pacific Conference on Web Intelligence: Research and Development
Clustering in massive data sets
Handbook of massive data sets
New unsupervised clustering algorithm for large datasets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
EURASIP Journal on Applied Signal Processing
Multi-level clustering and reasoning about its clusters using region connection calculus
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Hybrid agglomerative clustering for large databases: an efficient interactivity approach
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Hybrid O(n √ n) clustering for sequential web usage mining
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
| Hi-index | 0.14 |
In the above-titled paper, parallel implementations of hierarchical clustering algorithms that achieve O(n/sup 2/) computational time complexity and thereby improve on the baseline of sequential implementations are described. The latter are stated to be O(n/sup 3/), with the exception of the single-link method. The commenter points out that state-of-the-art hierarchical clustering algorithms have O(n/sup 2/) time complexity and should be referred to in preference to the O(n/sup 3/) algorithms, which were described in many texts in the 1970s. Some further references in the parallelizing of hierarchic clustering algorithms are provided.