Online Cluster Prototype Generation for the Gravitational Clustering Algorithm

  title={Online Cluster Prototype Generation for the Gravitational Clustering Algorithm},
  author={Elizabeth Le{\'o}n-Guzm{\'a}n and Jonatan G{\'o}mez and Fabi{\'a}n Giraldo},
Data clustering is a popular data mining technique for discovering the structure of a data set. However, the power of the results depends on the nature of the clusters prototypes generated by the clustering technique. Some cluster algorithms just label the data points producing a prototype for the cluster as the full set of data points belonging to the clusters. Some techniques produce a single ’abstract’ data point as the model for the full cluster losing the information of the shape, size and… 
1 Citations
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.


A multi-prototype clustering algorithm
Chameleon: Hierarchical Clustering Using Dynamic Modeling
Chameleon's key feature is that it accounts for both interconnectivity and closeness in identifying the most similar pair of clusters, which is important for dealing with highly variable clusters.
A multi-prototype clustering algorithm based on minimum spanning tree
A minimum spanning tree (MST) based multi-prototype clustering algorithm that can deal with datasets consisting of clusters with different shapes, sizes and densities is proposed.
Comparison of Agglomerative and Partitional Document Clustering Algorithms
Experimental evaluation shows that for every criterion function, partitional algorithms always lead to better clustering results than agglomerative algorithms, which suggests that partitional clustering algorithms are well-suited for clustering large document datasets due to not only their relatively low computational requirements, but also comparable or even better clustersering performance.
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.
Guided fuzzy clustering with multi-prototypes
A new fuzzy clustering algorithm using multi-prototype representation of clusters is proposed in this paper to discover clusters with arbitrary shapes and sizes. Intra-cluster non-consistency and
RAIN: data clustering using randomized interactions between data points
The Gravitational Clustering Algorithm is extended in such a way that the Gravitational Law is not the only law that can be applied and any decreasing function of the distance between points can be used.
Scalable evolutionary clustering algorithm with Self Adaptive Genetic Operators
The proposed scalable ECSAGO algorithm is able to find accurate representations of the clusters on very large data sets of different sizes and dimensionality that might not fit in main memory, while maintaining the desirable properties of robustness to noise and automatic detection of the number of clusters.
Gravitational clustering: a new approach based on the spatial distribution of the points
  • S. Kundu
  • Computer Science
    Pattern Recognit.
  • 1999
Data Mining: Concepts and Techniques
This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.