Journal ID : TRKU-16-03-2020-10542
[This article belongs to Volume - 62, Issue - 03]
Total View : 257

Title : Systematic Review of Unsupervised Genomic Clustering Algorithms Techniques for High Dimensional Datasets

Abstract :

High-dimensional data is interpreted with a considerable number of features, and new problems are presented in groups. The so-called "high dimension" is initially created to explain the common increase in time complexity of many computational problems, and therefore the performance of general aggregation algorithms is unsuccessful. Accordingly, many works focused on introducing new technologies and aggregation algorithms to process data with higher dimensions. Standard algorithms for all aggregate algorithms are the fact that they need a different essential evaluation of the similarity between data objects. However, current aggregation algorithms still have some open research problems. In this review work, they provide a summary of the results of the high-dimensional data space and its effects on different aggregation algorithms. It also provides a detailed overview of several grouping algorithms with several types: subspace methods, model-based grouping, density-based grouping methods, partition-based grouping methods, etc., including a more detailed description of the recent work of its advantages and disadvantages in Solve the problem of higher-dimensional data. The scope of future work is also discussed at the end of the work to expand existing compilation methods and algorithms

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