Graham J. Williams

Artificial Intelligence · 2000–2015

A researcher in the Science corpus: 5 papers; trusts 6 researchers and is trusted by 29 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 6 citations into it; #7,008 of 35,345 by trust here, #8,525 by citations

Whom Graham J. Williams trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Robert Hecht-Nielsen
2. David H. Ackley
3. Peter J. Rousseeuw
4. Kenji Yamanishi
5. Simon Hawkins
6. Geoffrey E. Hinton
7. Raymond T. Ng
8. Jun'ichi Takeuchi
9. Wen Jin
10. Terrence J. Sejnowski

Who trusts Graham J. Williams overall

1. Max Kuhn
2. Charu C. Aggarwal
3. Alireza Arabameri
4. E. Fehrman
5. Fei Tony Liu
6. Bernd Bischl
7. Varun Chandola
8. Kjell Johnson
9. Jacopo Castellini
10. Clifton Phua

Papers from Graham J. Williams's perspective

Ordered by how strongly the researchers Graham J. Williams trusts endorse each paper. Same corpus, a different order for every researcher.

1. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise
2. BIRCH: an efficient data clustering method for very large databases
3. A Learning Algorithm for Boltzmann Machines
4. On-Line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms (Knowledge Discovery and Data Mining)
5. Robust Regression and Outlier Detection (Wiley Series in Probability and Statistics)
6. Efficient algorithms for mining outliers from large data sets (ACM SIGMOD Conference)
7. UCI Repository of machine learning databases
8. Mining top-n local outliers in large databases (Knowledge Discovery and Data Mining)
9. LOF: identifying density-based local outliers
10. Replicator neural networks for universal optimal source coding.

Their papers

• Outlier Detection Using Replicator Neural Networks (2002) — 785 citations
• Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2015) — 63 citations
• Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery (2011) — 384 citations
• A comparative study of RNN for outlier detection in data mining (2002) — 308 citations
• On-Line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms (2000) — 718 citations