@article{MAKHILLJEAS2017122114954,
    title = {Minimum Spanning Tree Based Community Detection for Biological Data Analysis},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {12},
    number = {21},
    pages = {5452-5456},
    year = {2017},
    issn = {1816-949x},
    doi = {jeasci.2017.5452.5456},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2017.5452.5456},
    author = {Maria and},
    keywords = {Clustering,biological data,minimum spanning tree,performance evaluation,preprocessing step,compares and evaluates},
    abstract = {Bioinformatics is an important area in which computing techniques can be applied for efficient data
analysis and for mining meaningful patterns. The organization, analysis and interpretation of data are the major
challenges faced by biologists when dealing with large amount of heterogeneous and complex data.
Unsupervised learning techniques are widely used for data reduction and pattern extraction for in-depth
analysis and knowledge discovery. Graph clustering is a more suitable approach, since, the interactions of the
biological components can be effectively demonstrated by networks. The complexity of the graphs can be
reduced by extracting highly significant edges instead of focusing on all edges that represents the association
between data objects. This study compares and evaluates the significance of different Minimum Spanning Tree
algorithms (MST) as a preprocessing step for community detections in biological data. Multiple algorithms were
reviewed and compared and the process performance is compared with benchmark community detection
algorithms.}
    }