An efficient cell-based clustering method for handling large, high-dimensional data

Authors:
Jae-Woo Chang
Affiliations:
Dept. of Computer Engineering, Research Center of Industrial Technology, Engineering Research Institute, Chonbuk National University, Chonju, Chonbuk, South Korea
Venue:
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Year:
2003

Citing 4
Cited 0

BIRCH: an efficient data clustering method for very large databases

SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Automatic subspace clustering of high dimensional data for data mining applications

SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques

Data mining: concepts and techniques
STING: A Statistical Information Grid Approach to Spatial Data Mining

VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we propose an efficient cell-based clustering method for handling a large of amount of high-dimensional data. Our clustering method provides an efficient cell creation algorithm using a space-partitioning technique and a cell insertion algorithm to construct clusters as cells with more density than a given threshold. To achieve good retrieval performance on clusters, we also propose a new filtering-based index structure using an approximation technique. In addition, we compare the performance of our cell-based clustering method with the CLIQUE method in terms of cluster construction time, precision, and retrieval time. The experimental results show that our clustering method achieves better performance on cluster construction time and retrieval time.