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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

Cohesive Sub-network Mining in Protein Interaction Networks Using Score-Based Co-clustering with MapReduce Model (MR-CoC)

verfasst von : R. Gowri, R. Rathipriya

Erschienen in: Advances in Big Data and Cloud Computing

Verlag: Springer Singapore

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Abstract

Nowadays, due to the data deluge situation, every computation has to be carried out in voluminous data. The sub-network mining from the complex and voluminous interaction data is one of the research challenges. The highly connected sub-networks will be more cohesive in the network. They are responsible for communication among the network, which is useful for studying their functionalities. A novel score-based co-clustering (MR-CoC) technique with MapReduce is proposed to mine the highly connected sub-network from interaction networks. The MapReduce environment is chosen to cope with complex, voluminous data and to parallelize the computation process. This approach is used to mine cliques, non-cliques, and overlapping sub-network patterns from the adjacency matrix of the network. The complexity of the proposed work is O (Es + log Ns), which is minimal than the existing approaches like MCODE and spectral clustering.

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Vorheriges Kapitel SBKMEDA: Sorting-Based K-Median Clustering Algorithm Using Multi-Machine Technique for Big Data
Nächstes Kapitel Design and Development of Hybridized DBSCAN-NN Approach for Location Prediction to Place Water Treatment Plant
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Metadaten
Titel
Cohesive Sub-network Mining in Protein Interaction Networks Using Score-Based Co-clustering with MapReduce Model (MR-CoC)
verfasst von
R. Gowri
R. Rathipriya
Copyright-Jahr
2018
Verlag
Springer Singapore
Buch
Advances in Big Data and Cloud Computing

Print ISBN: 978-981-10-7199-7

Electronic ISBN: 978-981-10-7200-0

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-981-10-7200-0_20

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