CLUSTERING GENE EXPRESSION DATA USING AN EFFECTIVE DISSIMILARITY MEASURE1

Clustering Gene Expression Data Using an Effective Dissimilarity Measure1

This paper presents two clustering methods: the first one uses a density-based approach (DGC) and the second one uses a frequent itemset mining approach (FINN). DGC uses regulation information as well as order preserving ranking for identifying relevant clusters in gene expression data. FINN exploits the frequent itemsets and uses a nearest neighbour approach for clustering gene sets. Both the ...

Clustering Gene Expression Data Using an Effective Dissimilarity Measure

This paper presents two clustering methods: the first one uses a density-based approach (DGC) and the second one uses a frequent itemset mining approach (FINN). DGC uses regulation information as well as order preserving ranking for identifying relevant clusters in gene expression data. FINN exploits the frequent itemsets and uses a nearest neighbour approach for clustering gene sets. Both the ...

Effective Clustering Algorithms for Gene Expression Data

An Effective Technique for Clustering Incremental Gene Expression data

This paper presents a clustering technique (GenClus) for gene expression data which can also handle incremental data. It is designed based on density based approach. It retains the regulation information which is also the main advantage of the clustering. It uses no proximity measures and is therefore free of the restrictions offered by them. GenClus is capable of handling datasets which are up...

Clustering Gene Expression Data using Neural Networks

Microarray technology can be used to collect gene expression data in bulk. In order to be able to deal with this large amount of data that can now be produced, an efficient method of computing the relationships of this data must be constructed. Some attempts at applying neural networks have been employed for this task. For this project we intend to implement several neural network architectures...