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Background and objective
The major difficulty of the analysis of the input gene expression data in a microarray‐based approach for an automated diagnosis of cancer is the large number of genes (high dimensionality) with many irrelevant genes (noise) compared to the very small number of samples. This research study tackles the dimensionality reduction challenge in this area.
Methods
This research...
Ease of interpretation of results makes hierarchical clustering algorithms suitable for many applications. However they suffer high computational complexity. Single Linkage hierarchical clustering with its related graph theoretical terms is the most famous among hierarchical algorithms. Several existing techniques reduce its complexity by formulating it as a minimum spanning tree (MST) problem. The...
In gene expression analysis, grouping co-regulated genes is a major step in discovering genes which are likely to have related biological functions. This critical step can be done using clustering. This paper formally presents three models for iterative clustering based on average, single and complete linkage strategies. Variation of relational clustering algorithms can be built based on these models...
Symbolic data classification is of great importance in classification of massive high dimensional data that may exist in domains such as bioinformatics and web mining. Feature values (events) of symbolic data are generally not single values, as in the classical case, but rather list of values, intervals or, more generally, distributions. This study proposes a symbolic classification algorithm that...
Protein sequence clustering is a process that aims to identify sets of homologous proteins in a protein database. In this paper, two efficient soft c-mediods clustering algorithms for prototype selection for protein sequences are presented. In the proposed techniques patterns are considered to belong to some but not necessarily all clusters. The proposed algorithms is comprised of a judicious integration...
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