Optimized time efficient data cluster validity measures
Автор: Anand Khandare, A. S. Alvi
Журнал: International Journal of Information Technology and Computer Science @ijitcs
Статья в выпуске: 4 Vol. 10, 2018 года.
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The main task of any clustering algorithm is to produce compact and well-separated clusters. Well separated and compact type of clusters cannot be achieved in practice. Different types of clustering validation are used to evaluate the quality of the clusters generated by clustering. These measures are elements in the success of clustering. Different clustering requires different types of validity measures. For example, unsupervised algorithms require different evaluation measures than supervised algorithms. The clustering validity measures are categorized into two categories. These categories include external and internal validation. The main difference between external and internal measures is that external validity uses the external information and internal validity measures use internal information of the datasets. A well-known example of the external validation measure is Entropy. Entropy is used to measure the purity of the clusters using the given class labels. Internal measures validate the quality of the clustering without using any external information. External measures require the accurate value of the number of clusters in advance. Therefore, these measures are used mainly for selecting optimal clustering algorithms which work on a specific type of dataset. Internal validation measures are not only used to select the best clustering algorithm but also used to select the optimal value of the number of clusters. It is difficult for external validity measures to have predefined class labels because these labels are not available often in many of the applications. For these reasons, internal validation measures are the only solution where no external information is available in the applications. All these clustering validity measures used currently are time-consuming and especially take additional time for calculations. There are no clustering validity measures which can be used while the clustering process is going on. This paper has surveyed the existing and improved cluster validity measures. It then proposes time efficient and optimized cluster validity measures. These measures use the concept of cluster representatives and random sampling. The work proposes optimized measures for cluster compactness, separation and cluster validity. These three measures are simple and more time efficient than the existing clusters validity measures and are used to monitor the working of the clustering algorithms on large data while the clustering process is going on.
Clustering Algorithm, Cluster, Validity Measure, Runtime, Compactness, Separation
Короткий адрес: https://sciup.org/15016253
IDR: 15016253 | DOI: 10.5815/ijitcs.2018.04.05
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