X-means: Extending K-means with Efficient Estimation of the Number of Clusters

By Dan Pelleg, Andrew W. Moore. 2000

2,899 citations recorded by the source corpus.

How researchers see this paper

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

Citations in the corpus

Sigma-point kalman filters for probabilistic inference in dynamic state-space models (2004, methodology): “number of Gaussian compone nts) through the use of some probabilistic cost function such as AIC or BIC [28, 32].”
Learning the k in k-means (2003, methodology): “In these experiments, we show that the BIC is ineffective as a scoring function, since it does not penalize strongly enough the model’s complexity.”
Segmenting Motion Capture Data into Distinct Behaviors (2004, methodology): “As an alternative, GMM is often estimated for various values of k, and the mixture model that maximizes some criterion , such as the Bayesian Information Criterion (BIC) [22] is chosen.”
Data clustering: 50 years beyond K-means (2008, unknown): “Bradley et al. (1998) presented a fast scalable and singlepass version of K-means that does not require all the data to be fit in the memory at the same time.”
Dynamic clustering using particle swarm optimization with application in image segmentation (2006, methodology): “Furthermore, Pelleg and Moore [33] proposed a K-means based algorithm, called X-means, that uses model selection.”
Advances in Intelligent Data Analysis V (2003, methodology): “– Hierarchical clustering methods “produce a nested data set in which pairs of items or clusters are successively linked until every item in the data set is connected” [20].”
Human mobility modeling at metropolitan scales (2012, methodology): “From this, we use a common clustering algorithm (X-means [27]) to cluster users.”
Learning the semantics of object–action relations by observation (2011, background): “Note, as soon as the complete table has been measured, it is also accessible to unsupervised classification (X -means) (Dan Pelleg 2000).”
EBK-Means: A Clustering Technique based on Elbow Method and K-Means in WSN (2014, background): “In [9], Pelleg and Moore work on a regularization framework for learning k and they call it X-means.”
From Throw-Away Traffic to Bots: Detecting the Rise of DGA-Based Malware (2012, methodology): “We then apply the X-means clustering algorithm [24] to”
Learning Object Affordances: From Sensory--Motor Coordination to Imitation (2008, methodology): “Since our setup is clearly discrete, we applie d the X-meansalgorithm [37] to detect clusters in the space of each object feature and in the effects.”
Instance-Specific Algorithm Configuration (2014, background): “Therefore, to address this issue, X-means has been introduced in [89].”