Applying clustering algorithm to analyze the data from different dimensions

Mago, B (2019) Applying clustering algorithm to analyze the data from different dimensions. In: 2019 International Conference on Digitization (ICD), 18-19 November 2019, Sharjah, United Arab Emirates.

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In today's data-driven panorama, the ability to analyze information to drive decision-making and resolve problems is fundamental for success. This requires a robust, effective, flexible, data analytics that assists to build accurate predictive versions quickly and intuitively. Data analysis is a common technique used to analyze data in various fields of modern scientific research, which includes different divisions of Data analytics include many techniques. Clustering is considered as one of the unsupervised learning technique for analyzing the data. With the increase in number of disciplines, the amount of data is also increased. This results in the development of various tools and algorithms for applying cluster analysis. Each of the clustering algorithm has its own advantages and limitations and it completely depends on the complexity of available information. The current research is an attempt to analyze the data using clustering techniques. The researcher use python language to compile a program to collect the data from an enterprise's information management system. Python is used to analyze and clusters are interpreted accordingly. The results of clustering data based on different dimensions will lead to improve knowledge about the data accordingly.

Affiliation: Skyline University College
SUC Author(s): Mago, B ORCID:
All Author(s): Mago, B
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Clustering, Unsupervised algorithm, Unsupervised learning, Python, K Means.
Subjects: B Information Technology > BB Information Technology
B Information Technology > BQ Data Analytics
Divisions: Skyline University College > School of IT
Depositing User: Mr Veeramani Rasu
Date Deposited: 28 Nov 2021 08:47
Last Modified: 28 Nov 2021 08:47
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