Ordering Grids to Identify the Clustering Structure

Authors:
Shihong Yue;Miaomiao Wei;Yi Li;Xiuxiu Wang
Affiliations:
School of Electric Engineering and Automation, Tianjin University, Tianjin300072, China;School of Electric Engineering and Automation, Tianjin University, Tianjin300072, China;School of Electric Engineering and Automation, Tianjin University, Tianjin300072, China;School of Electric Engineering and Automation, Tianjin University, Tianjin300072, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

Citing 6
Cited 0

OPTICS: ordering points to identify the clustering structure

SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Stability-based validation of clustering solutions

Neural Computation
Automated Variable Weighting in k-Means Type Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
A cluster validation index for GK cluster analysis based on relative degree of sharing

Information Sciences—Informatics and Computer Science: An International Journal
An objective approach to cluster validation

Pattern Recognition Letters
Survey of clustering algorithms

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Almost all of the well-known clustering algorithms require input parameters while these parameters may be difficult to be determined. OPTICS (Ordering Points To Identify the Clustering Structure Cluster Structure) is a primary semi-clustering method to visualize the data structure and to determine the input parameters of a given clustering algorithm. However, OPTICS has too high complexity O(n2logn) to be applied to any large dataset of ndata. In this paper, we present a new semi-clustering method by partitioning data space into a number of grids and Ordering all Grids To Identify the Clustering Structure (OGTICS). Accordingly, the new method is called OGTICS. The OGTICS has only linear complexity O(n) and thus is much faster than OPTICS. Consequently, the OGTICS can be applied to very large dataset.