A Density-based Hierarchical Clustering Algorithm for Highly Overlapped Distributions with Noisy Points

Authors:
Damaris Pascual;Filiberto Pla;J. Salvador Sánchez
Affiliations:
Centro de Reconocimiento de Patrones, Universidad de Oriente, Cuba;Institute of New Imaging Technologies, Dept. Llentguages i Sistemas Informátics, Universitat Jaume I of Castelló, Spain;Institute of New Imaging Technologies, Dept. Llentguages i Sistemas Informátics, Universitat Jaume I of Castelló, Spain
Venue:
Proceedings of the 2010 conference on Artificial Intelligence Research and Development: Proceedings of the 13th International Conference of the Catalan Association for Artificial Intelligence
Year:
2010

Citing 13
Cited 0

CURE: an efficient clustering algorithm for large databases

SIGMOD '98 Proceedings of the 1998 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 clustering: a review

ACM Computing Surveys (CSUR)
Unsupervised Learning of Finite Mixture Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
Chameleon: Hierarchical Clustering Using Dynamic Modeling

Computer
Efficient and Effective Clustering Methods for Spatial Data Mining

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A New Cluster Isolation Criterion Based on Dissimilarity Increments

IEEE Transactions on Pattern Analysis and Machine Intelligence
Bagging for Path-Based Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Grid-Clustering: An Efficient Hierarchical Clustering Method for Very Large Data Sets

ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
KNN-kernel density-based clustering for high-dimensional multivariate data

Computational Statistics & Data Analysis
Non parametric local density-based clustering for multimodal overlapping distributions

IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Image segmentation via adaptive K-mean clustering and knowledge-based morphological operations with biomedical applications

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this work, we present a new two-stage technique to find clusters of different shapes, densities and sizes in the presence of overlapped clusters and noise. Firstly, a density-based clustering approach is developed using a density function estimated by the EM algorithm and in the second stage, a hierarchical strategy is used to merge clusters according to a dissimilarity measure here introduced in order to assess the overlap and proximity of the clusters. Several synthetic and real world data sets are used to evaluate the effectiveness and the efficiency of the new algorithm, indicating that it obtains satisfactory clustering results.