Similarity Measures for Text Document Clustering
By Anna-Lan Huang. 2008
1,290 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/b29bf8f6900b4b0258397f73957eabd1bb977ef4
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- Mackenzie W. Mathis
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- Martin Degeling
- Mu Zhou
- Alexander Mathis
- Shaokai Ye
- Tanmay Nath
Citations in the corpus
- Multi-animal pose estimation, identification and tracking with DeepLabCut (2022, background): “…computed the number of ground truth keypoints left unconnected (after assembly) and purity—an additional criterion for quality that can be understood as the accuracy of the assignment of all keypoints of a putative subset to the most frequent ground truth animal identity within that subset 41 .”
- Analysis of Similarity Measures in Times Series Clustering for the Discovery of Building Energy Patterns (2013, background): “Other noteworthy options are the cosine measure [22], which is good for patterns with different or variable size or length; or Jaccard and Tanimoto similarity measures, that can also be intuitively understood as a combination of Euclidean distances and correlations assessed by means of the inner…”
- Feature selection for text classification: A review (2018, background): “…proposed and we focus on some popular measures for text processing, for example, Euclidean distance, Jaccard coefficient [54], Pearson correlation [40], Cosine Similarity [6], Hamming distance [62], Dice coefficient [85], IT-Sim [3, 56], SMTP [58], Earth mover’s distance [29, 96],…”
- We Value Your Privacy ... Now Take Some Cookies (2018, methodology): “ve that contained additional information about the data source. To compare different versions of policies and policies from different websites we used the Jaccard similarity index on a sentence level [19], which is commonly used to identify plagiarism [24]. The Jaccard index measures similarity as t”
- The contribution of neuroscience to consumer research: A conceptual framework and empirical review (2013, background): “The degree of resemblance between word meanings is formalised through metrics called similarity measures (Huang, 2008), which in turn can be weighted through lexical databases (Miller, 1995).”