A new approach to clustering data with arbitrary shapes

Authors:
Mu-Chun Su;Yi-Chun Liu
Affiliations:
Department of Computer Science & Information Engineering, National Central University, Chung-Li, Taiwan, R.O.C.;Department of Computer Science & Information Engineering, National Central University, Chung-Li, Taiwan, R.O.C.
Venue:
Pattern Recognition
Year:
2005

Citing 17
Cited 0

Algorithms for clustering data

Algorithms for clustering data
Unsupervised Optimal Fuzzy Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
Extraction of early perceptual structure in dot patterns: integrating region, boundary, and component gestalt

Computer Vision, Graphics, and Image Processing
Interpreting the Kohonen self-organizing feature map using contiguity-constrained clustering

Pattern Recognition Letters
Data induced metric and fuzzy clustering of non-convex patterns of arbitrary shape

Pattern Recognition Letters
A Modified Version of the K-Means Algorithm with a Distance Based on Cluster Symmetry

IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
Clustering Algorithms

Clustering Algorithms
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters

IEEE Transactions on Computers
A Nonlinear Mapping for Data Structure Analysis

IEEE Transactions on Computers
A note on the Gustafson-Kessel and adaptive fuzzy clustering algorithms

IEEE Transactions on Fuzzy Systems
Hierarchical unsupervised fuzzy clustering

IEEE Transactions on Fuzzy Systems
A self-organizing network for hyperellipsoidal clustering (HEC)

IEEE Transactions on Neural Networks
A new neural network for cluster-detection-and-labeling

IEEE Transactions on Neural Networks
A new model of self-organizing neural networks and its application in data projection

IEEE Transactions on Neural Networks
Artificial neural networks for feature extraction and multivariate data projection

IEEE Transactions on Neural Networks
A nonlinear projection method based on Kohonen's topology preserving maps

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper we propose a clustering algorithm to cluster data with arbitrary shapes without knowing the number of clusters in advance. The proposed algorithm is a two-stage algorithm. In the first stage, a neural network incorporated with an ART-like training algorithm is used to cluster data into a set of multi-dimensional hyperellipsoids. At the second stage, a dendrogram is built to complement the neural network. We then use dendrograms and so-called tables of relative frequency counts to help analysts to pick some trustable clustering results from a lot of different clustering results. Several data sets were tested to demonstrate the performance of the proposed algorithm.