Abstract
Clustering using K-means algorithm is very common way to understand and analyze the obtained output data. This clustering is done by making a sample collection of similar objects in one slot and non-similar objects in another slot. The slot can have say let N number of data objects and say let K number of clusters (slot) are formed to make a bunch of common data where k < n. Each cluster has its own centroid, but we cannot predict how exactly cluster shall be based on data. Cluster formation has random occurence, i.e., if one object is dissimilar from rest of all other cluster objects, then the dissimilar object must have its own cluster. This is the main drawback of this approach, and we cannot get optimal result from this clustering approach. To overcome the drawback challenge, we are proposing a formula to find the clusters at the run time, so this approach can give us optimal results. The proposed approach uses Euclidian distance formula as well Mehlanobis to find the minimum distance between slots as technically we called as clusters, and we have also applied the same approach to ant colony optimization (ACO) algorithm which results in the production of two- and multi-dimensional matrix.
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Yadav, K., Gupta, S., Gupta, N., Gupta, S.L., Khandelwal, G. (2021). Hybridization of K-means Clustering Using Different Distance Function to Find the Distance Among Dataset. In: Senjyu, T., Mahalle, P.N., Perumal, T., Joshi, A. (eds) Information and Communication Technology for Intelligent Systems. ICTIS 2020. Smart Innovation, Systems and Technologies, vol 195. Springer, Singapore. https://doi.org/10.1007/978-981-15-7078-0_29
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