A Comparison of Heuristics with Modularity Maximization Objective using Biological Data Sets
Finding groups of objects exhibiting similar patterns is an important data analytics task. Many disciplines have their own terminologies such as cluster, group, clique, community etc. defining the similar objects in a set. Adopting the term community, many exact and heuristic algorithms are develope...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2016-01-01
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| Series: | MATEC Web of Conferences |
| Online Access: | http://dx.doi.org/10.1051/matecconf/20164205001 |
| Summary: | Finding groups of objects exhibiting similar patterns is an important data analytics task. Many disciplines have their own terminologies such as cluster, group, clique, community etc. defining the similar objects in a set. Adopting the term community, many exact and heuristic algorithms are developed to find the communities of interest in available data sets. Here, three heuristic algorithms to find communities are compared using five gene expression data sets. The heuristics have a common objective function of maximizing the modularity that is a quality measure of a partition and a reflection of objects’ relevance in communities. Partitions generated by the heuristics are compared with the real ones using the adjusted rand index, one of the most commonly used external validation measures. The paper discusses the results of the partitions on the mentioned biological data sets. |
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| ISSN: | 2261-236X |