# RAIN: data clustering using randomized interactions between data points

@article{Gmez2004RAINDC, title={RAIN: data clustering using randomized interactions between data points}, author={Jonatan G{\'o}mez and O. Nasraoui and Elizabeth Le{\'o}n Guzman}, journal={2004 International Conference on Machine Learning and Applications, 2004. Proceedings.}, year={2004}, pages={250-255} }

This paper introduces a generalization of the Gravitational Clustering Algorithm. First, it is extended in such a way that the Gravitational Law is not the only law that can be applied. Instead, any decreasing function of the distance between points can be used. An estimate of the maximum distance between the closest points is calculated in order to reduce the sensibility of the clustering process to the size of the data set. Finally, a heuristic for setting the interaction strength…

## 13 Citations

Spherical Randomized Gravitational Clustering

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A variation of the randomized gravitational clustering algorithm is proposed that uses the cosine distance, the gravitational law is modified in order to use theCosine distance and geodesics ('straight' lines in curved spaces) are used inorder to move points according to the gravitational dynamic.

The Parameter-less Randomized Gravitational Clustering algorithm with online clusters’ structure characterization

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Minimum Cluster Size Estimation and Cluster Refinement for the Randomized Gravitational Clustering Algorithm

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The proposed Randomized Gravitational Clustering algorithm is able to deal with noise, while finding an appropriate number of clusters without requiring a manual setting of the minimum cluster size.

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This paper proposes a three-stage hierarchical clustering approach called GHC, which takes advantage of the vector characteristic of data gravitational force inspired by the law of universal gravitation and achieves better performance than the other existing clustering algorithms.

Online Cluster Prototype Generation for the Gravitational Clustering Algorithm

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An on-line cluster prototype generation mechanism for the Gravitational Clustering algorithm that uses the gravitational system dynamic and the inherent hierarchical property of the gravitational algorithm for determining some summarized prototypes of clusters at the same time the gravitational clustering algorithm is finding such clusters.

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Inspired by universal gravitation, a novel clustering algorithm (called DCLRF) based on density core and local resultant force is proposed based on LRF and natural neighbors to obtain the optimal cluster numbers for the datasets which contain clusters of arbitrary shapes.

Fuzzy granular gravitational clustering algorithm

- Computer Science2012 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS)
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A proposed gravitational model for finding clusters is introduced, the algorithm is based on the gravitational forces from Newton's law of universal gravitation and the output clusters are then fuzzified.

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