Clustering Algorithms
Finding Consistent Clusters in Data Partitions
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Data Clustering Using Evidence Accumulation
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Combining Multiple Weak Clusterings
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Moderate diversity for better cluster ensembles
Information Fusion
A Fuzzy Approach for Analyzing Outliers in Gene Expression Data
BMEI '08 Proceedings of the 2008 International Conference on BioMedical Engineering and Informatics - Volume 01
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In the presence of huge high dimensional datasets, it is important to investigate and visualize the connectivity of patterns in huge arbitrary shaped clusters. While density or distance-relatedness based clustering algorithms are used to efficiently discover clusters of arbitrary shapes and densities, classical (yet less efficient) clustering algorithms can be used to analyze the internal cluster structure and visualize it. In this work, a sequential ensemble, that uses an efficient distance-relatedness based clustering, “Mitosis”, followed by the centre-based K-means algorithm, is proposed. K-means is used to segment the clusters obtained by Mitosis into a number of subclusters. The ensemble is used to reveal the gradual change of patterns when applied to gene expression sets.