Performance in ART1-Like Document Clustering with Variable Similarity Thresholds

Authors

  • Kevin MacLeod

DOI:

https://doi.org/10.29173/cais150

Abstract

The availability of effective clusters is crucial to many document retrieval systems. MacLeod's algorithm incorporates ART1-like training with static similarity thresholds to form effective document clusters and is well suited to massively parallel implementations [1]. This paper reports on a study of the effectiveness of clusters formed by the algorithm when several variable threshold functions are. . .

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Published

2013-10-24

How to Cite

MacLeod, K. (2013). Performance in ART1-Like Document Clustering with Variable Similarity Thresholds. Proceedings of the Annual Conference of CAIS Actes Du congrès Annuel De l’ACSI. https://doi.org/10.29173/cais150

Issue

Section

Articles