k-means-: A Unified Approach to Clustering and Outlier Detection

By Sanjay Chawla, Aristides Gionis. 2013

282 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Civil and Structural Engineering, Computer Networks and Communications, Anomaly & Outlier Detection

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Han-Jia Ye
2. De-chuan Zhan
3. Richard S. Zemel
4. Sergey Levine
5. Josh Tenenbaum
6. Yoshua Bengio
7. Bernt Schiele
8. Trevor Darrell

In Civil and Structural Engineering

1. Josh Tenenbaum
2. Ali Anaissi
3. Mengye Ren
4. Nguyen Phan Dang Khoa
5. Yang Wang

In Computer Networks and Communications

1. Ravi Kumar
2. Sergei Vassilvitskii
3. Kevin Swersky
4. Josh Tenenbaum
5. Hugo Larochelle
6. Richard S. Zemel
7. Ali Anaissi
8. Nguyen Phan Dang Khoa

In Anomaly & Outlier Detection

1. Hugo Larochelle
2. Ali Anaissi
3. Nguyen Phan Dang Khoa
4. Yang Wang

Citations in the corpus

• Meta-Learning for Semi-Supervised Few-Shot Classification (2018, methodology): “In addition to the original k-Means method (Lloyd, 1982), the most related work to our setup involving clustering algorithms considers applying k-Means in the presence of outliers (Hautamäki et al., 2005; Chawla & Gionis, 2013; Gupta et al., 2017).”
• Smart Infrastructure Maintenance Using Incremental Tensor Analysis: Extended Abstract (2017, methodology): “Chawla and Gionis [9] proposed a method, named k-means−−, as a unified approach for simultaneously clustering and detecting anomalies in data.”
• Local Search Methods for k-Means with Outliers (2017, methodology): “Given that, the precision and recall scores are fairly good (especially for k = 15), and the method consistently outperforms that of [12].”