Efficient clustering of high-dimensional data sets with application to reference matching

By Andrew McCallum, Kamal Nigam, Lyle Ungar. Knowledge Discovery and Data Mining, 2000

1,240 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/8856b09c032ed4f10ef8367a8f7088fbb891ec2b

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Citations in the corpus

Research on K-Value Selection Method of K-Means Clustering Algorithm (2019, background): “Repeat the above steps until the list of candidate center vectors is empty; that is, X is empty and the algorithm ends [19].”
Semi-Supervised Self-Training of Object Detection Models (2005, background): “In recent years there has been a substantial amount of work that addresses the problem of incorporating unlabeled data into the training process, [14], [9], [11].”
Two supervised learning approaches for name disambiguation in author citations (2004, neutral): “Much research has been done to address the identity uncertainty problem in different fields using different methods, such as record linkage [17], duplicate record detection and elimination [8, 26, 29], merge/purge [22], data association [6], database hardening [11], citation matching [28], name matc”
Probabilistic models with unknown objects (2006, methodology): “in a preprocessing step, we group the citations into loose, potentially overlapping clusters called canopies [McCallum et al., 2000] based on the fraction of words that they have in common.”
Evaluation of entity resolution approaches on real-world match problems (2010, methodology): “Most previous studies used a single match approach like thresholdbased attribute matching (similarity join [5]), clustering [26], or context-based matching [1], [12], [33].”
A Latent Dirichlet Model for Unsupervised Entity Resolution (2005, background): “In addition, data integration is an area of active research [17, 26, 23].”
Reference reconciliation in complex information spaces (2005, methodology): “A large number of approaches that have been since proposed [3, 21, 27, 36, 35] use some variant of the original Felligi-Sunter model.”
Entity Disambiguation for Knowledge Base Population (2010, background): “A related approach is that of canopies for scaling clustering for large amounts of bibliographic citations (McCallum et al., 2000).”
Entity Linking: Finding Extracted Entities in a Knowledge Base (2013, background): “A related approach is that of canopies for scaling clustering for large amounts of bibliographic citations [29].”
Adaptive Blocking: Learning to Scale Up Record Linkage (2006, background): “, edit distance [6, 19] or Jaccard similarity [24], while others assume that forming blocks based on lexicographic sorting of records on some field(s) yields an optimal strategy [15, 37].”
A Comparison of Fast Blocking Methods for Record Linkage (2003, methodology): “Recent work by McCallum, Nigam and Unger[7], Cohen and Richman[2], and others have proposed the use of high-dimensional similarity indexing to improve the efficiency of blocking methods.”