Finding the Number of Clusters in a Dataset
By Catherine A. Sugar, Gareth M. James. 2003
833 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/1bdf38a4ab902987076dc3365fcf78dc2d8a3c10
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
- Christian Hennig
- T. Liao
- Cristian Preda
- J. Jacques
- Boris G. Mirkin
- Nantachai Kantanantha
- Yihua Chen
- Nicoleta Serban
In Statistics and Probability
- T. Liao
- Cristian Preda
- Nantachai Kantanantha
- Boris G. Mirkin
- Yihua Chen
- Christian Hennig
- Nicoleta Serban
- J. Jacques
Citations in the corpus
- Intelligent Choice of the Number of Clusters in K-Means Clustering: An Experimental Study with Different Cluster Spreads (2010, methodology): “(D) The Jump Statistic ( Sugar and James 2003 ) utilizes the criterion W in (1) extended according to”
- Functional data clustering: a survey (2013, background): “In Tarpey and Kinateder (2003) the authors investigate the use of d 0 with k-means for Gaussian processes.”
- Yield and Price Forecasting for Stochastic Crop Decision Planning (2010, methodology): “There are techniques that can be used to determine the number of clusters, for example, gap statistic (Tibshirani, Walther, and Hastie 2001), and jump method ( Sugar and James 2003 ), but here we choose a small value for K. We choose q by investigating the smoothness level under a series of values f”
- Nonrandom Local Circuits in the Dentate Gyrus (2008, background): “Dividing hilar cells into two groups produces a larger incremental decrease in distortion rate (“jump statistic”; Sugar and James, 2003) than treating all hilar cells as one group or positing three groups.”
- Theory and Use of the EM Algorithm (2011, background): “But if not, choosing the number of clusters k to assume is a difficult problem; see [56] for further discussion.”
- How to find an appropriate clustering for mixed‐type variables with application to socio‐economic stratification (2013, background): “Some of them are listed (though treated there in connection with k-means) in Sugar and James (2003); an older but more comprehensive simulation study is Milligan and Cooper (1985).”
- A Framework for Feature Selection in Clustering (2010, neutral): “…scope of this paper, and has been discussed extensively in the literature for standard K -means clustering; we refer the interested reader to Milligan and Cooper (1985), Kaufman and Rousseeuw (1990), Tibshirani, Walther, and Hastie (2001), Sugar and James (2003), and Tibshirani and Walther (2005).”