Abstract
Numerous of clustering methods are proficiently work for low dimensional data. However Clustering High dimensional data is still challenging related to time complexity and accuracy. This paper presents the performance efficiency and effectiveness of K-means and Agglomerative hierarchical clustering methods based on Euclidean distance function and quality measures Precision, Recall and F-measure for Microarray datasets by varying cluster values. Efficiency concerns about computational time required to build up dataset and effectiveness concerns about accuracy to cluster the data. Experimental results on Microarray datasets reveal that K-means clustering algorithm is favorable in terms of effectiveness where as efficiency of clustering algorithms depends on dataset used for empirical study.
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Chormunge, S., Jena, S. (2016). Performance Efficiency and Effectiveness of Clustering Methods for Microarray Datasets. In: Nagar, A., Mohapatra, D., Chaki, N. (eds) Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics. Smart Innovation, Systems and Technologies, vol 44. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2529-4_58
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DOI: https://doi.org/10.1007/978-81-322-2529-4_58
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