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About Gene Clustering Review Paper
This document provides a review of clustering algorithms that are commonly used for analyzing gene expression data. It begins with an introduction to DNA microarray technology and how it is used to study gene expression. It then discusses how clustering algorithms can group genes with similar expression patterns together. The main clustering algorithms described are K-means, hierarchical clustering including agglomerative and divisive approaches, CURE (Clustering Using Representatives), and CHAMELEON. The document provides a brief overview of how each algorithm works and its applications in analyzing gene expression data.
Detailed Information
| Author: | ['AYUSHMAN KOUL'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 4 |
| Language: | English |
| Format: | |
| Price: | FREE |
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