Probabilistic analysis of the RNN-CLINK clustering algorithm

Sheau-Dong Lang; Li-Jen Mao; Wen-Lin Hsu

doi:10.1117/12.339988

25 February 1999 Probabilistic analysis of the RNN-CLINK clustering algorithm

Sheau-Dong Lang, Li-Jen Mao, Wen-Lin Hsu

Proceedings Volume 3695, Data Mining and Knowledge Discovery: Theory, Tools, and Technology; (1999) https://doi.org/10.1117/12.339988
Event: AeroSense '99, 1999, Orlando, FL, United States

Abstract

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(N²)-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(N³)-time in the worst case, although the average time appears to be O(N²-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.

Citation Download Citation

Sheau-Dong Lang, Li-Jen Mao, and Wen-Lin Hsu "Probabilistic analysis of the RNN-CLINK clustering algorithm", Proc. SPIE 3695, Data Mining and Knowledge Discovery: Theory, Tools, and Technology, (25 February 1999); https://doi.org/10.1117/12.339988

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