An approach to find embedded clusters using density based techniques

Authors:
S. Roy;D. K. Bhattacharyya
Affiliations:
Dept of Computer Science & Information Technology, Tezpur University, Napaam, Assam, India;Dept of Computer Science & Information Technology, Tezpur University, Napaam, Assam, India
Venue:
ICDCIT'05 Proceedings of the Second international conference on Distributed Computing and Internet Technology
Year:
2005

Citing 6
Cited 8

CURE: an efficient clustering algorithm for large databases

SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
OPTICS: ordering points to identify the clustering structure

SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques

Data mining: concepts and techniques
Chameleon: Hierarchical Clustering Using Dynamic Modeling

Computer
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases

VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Cluster Analysis for Gene Expression Data: A Survey

IEEE Transactions on Knowledge and Data Engineering

DECODE: a new method for discovering clusters of different densities in spatial data

Data Mining and Knowledge Discovery
DGDCT: a distributed grid-density based algorithm for intrinsic cluster detection over massive spatial data

ICDCN'08 Proceedings of the 9th international conference on Distributed computing and networking
On cluster tree for nested and multi-density data clustering

Pattern Recognition
Using structure-based data transformation method to improve prediction accuracies for small data sets

Decision Support Systems
A grid-density based technique for finding clusters in satellite image

Pattern Recognition Letters
Multi-scale decomposition of point process data

Geoinformatica
CoBi: Pattern Based Co-Regulated Biclustering of Gene Expression Data

Pattern Recognition Letters
Clustering in extreme learning machine feature space

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents an efficient clustering technique which can identify any embedded and nested cluster over any variable density space. The proposed algorithm is basically an enhanced version of DBSCAN [4] and OPTICS [7]. Experimental results are reported to establish that the proposed clustering technique outperforms both DBSCAN and OPTICS in terms of complex cluster detection.