The application of classical clustering algorithms in big data analysis

The next scientific seminar of Department No. 13 was held at the Institute of Information Technology of ANAS. A presentation  “The Possibilities of Using Classical Clustering Algorithms in Big Data Analysis” was presented by Aygul Fakhraddingizi, a graduate student of the Institute.

The speaker gave information about data mining, data analytics, clustering, its algorithms and capabilities. She noted that clustering algorithms are an alternative, more powerful meta-learning tool for accurate analysis of large volumes of data using new technologies. According to her, the clustering algorithm, which provides information about the structure (sequence) of the data set, is one of the most commonly used methods of data analysis (data).

She talked about algorithms based on division, density, hierarchical and other clustering algorithms, gave detailed information about K-Means, Cure, DBSCAN, OptiGrid, etc., which are examples of these algorithms.

A. Fakhraddingizi said that in the hierarchical clustering algorithm, data is organized hierarchically depending on the degree of proximity. As the hierarchy continues, the initial cluster is gradually divided into several groups.

She also briefed on World Bank methods, indexation and rating based on confidence in measuring social capital.

© All rights reserved. Citing to www.ict.az is necessary upon using new