RAIN: data clustering using randomized interactions between data points

  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.},
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… 

Figures from this paper

Spherical Randomized Gravitational Clustering
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
This paper presents a data clustering algorithm that does not require a parameter setting process [the Parameter-less Randomized Gravitational Clustering algorithm (Pl-Rgc) and combines it with a mechanism, based in micro-clusters ideas, for representing a cluster as a set of prototypes.
Minimum Cluster Size Estimation and Cluster Refinement for the Randomized Gravitational Clustering Algorithm
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.
A Novel Hierarchical Clustering Approach Based on Universal Gravitation
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
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.
A density-core-based clustering algorithm with local resultant force
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
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.


Gravitational clustering: a new approach based on the spatial distribution of the points
  • S. Kundu
  • Computer Science
    Pattern Recognit.
  • 1999
A robust estimator based on density and scale optimization and its application to clustering
A new robust algorithm that estimates the prototype parameters of a given structure from a possibly noisy data set by dynamically estimates a scale parameter and the weights/memberships associated with each data point, and softly rejects outliers based on these weights.
A novel approach to unsupervised robust clustering using genetic niching
  • O. Nasraoui, R. Krishnapuram
  • Computer Science
    Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063)
  • 2000
A new unsupervised robust clustering algorithm that can successfully find dense areas (clusters) in feature space and determine their number and can handle a vast array of general subjective, even non-metric dissimilarities, and is thus useful in many applications such as Web and data mining.
Robust Clustering with Applications in Computer Vision
A clustering algorithm based on the minimum volume ellipsoid (MVE) robust estimator is proposed that was successfully applied to several computer vision problems formulated in the feature space paradigm: multithresholding of gray level images, analysis of the Hough space, and range image segmentation.
A Least Biased Fuzzy Clustering Method
A fuzzy clustering algorithm with minimal biases is formulated by making use of the maximum entropy principle to maximize the entropy of the centroids with respect to the data points (clustering entropy).
A possibilistic approach to clustering
An appropriate objective function whose minimum will characterize a good possibilistic partition of the data is constructed, and the membership and prototype update equations are derived from necessary conditions for minimization of the criterion function.
Clustering by competitive agglomeration
Some methods for classification and analysis of multivariate observations
The main purpose of this paper is to describe a process for partitioning an N-dimensional population into k sets on the basis of a sample. The process, which is called 'k-means,' appears to give
Introduction to Algorithms
The updated new edition of the classic Introduction to Algorithms is intended primarily for use in undergraduate or graduate courses in algorithms or data structures and presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers.
Soft computing techniques for intrusion detection
The main goals of this research is to define a set of data mining techniques based on soft computing concepts and to define a mechanism for integrating them for solving the problem of intrusion