Probabilistic analysis of the RNN-CLINK clustering algorithm
نویسندگان
چکیده
Clustering is among the oldest techniques used in data mining applications. Typical implementations of the hierarchical agglomerative clustering methods (HACM) require an amount of O(N)-space when there are N data objects, making such algorithms impractical for problems involving large datasets. The well-known clustering algorithm RNN-CLINK requires only O(N)-space but O(N)-time in the worst case, although the average time appears to be O(N log N). We provide a probabilistic interpretation of the average-time complexity of the algorithm. We also report experimental results using the randomly generated bit vectors and using the NETNEWS articles as the input, to support our theoretical analysis.
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