Mohamed Ali Abbas

Mohamed Ali Abbas
Alexandria University | AU · Department of Information Technology

Doctor of Philosophy
Experienced Data Analyst with a demonstrated history of working in the government administration industry.

About

7
Publications
1,144
Reads
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49
Citations
Citations since 2017
4 Research Items
43 Citations
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Introduction
Experienced Data Analyst with a demonstrated history of working in the government administration industry. Skilled in Python, C++ and Statistical Data Analysis. Strong information technology professional with a MSc in Computer Science and PhD in Information Technology. Focused in data mining, machine learning and pattern recognition. A Recognized Instructor in many reputable organizations.
Education
October 2022 - October 2022
institute of graduate studies and research
Field of study
  • Information Technology

Publications

Publications (7)
Article
Full-text available
[Source code: https://github.com/egy1st/denmune-clustering-algorithm]. Many clustering algorithms fail when clusters are of arbitrary shapes, of varying densities, or the data classes are unbalanced and close to each other, even in two dimensions. A novel clustering algorithm ”DenMune” is presented to meet this challenge. It is based on identifyi...
Article
Full-text available
A novel clustering algorithm CSHARP is presented for the purpose of finding clusters of arbitrary shapes and arbitrary densities in high dimensional feature spaces. It can be considered as a variation of the Shared Nearest Neighbor algorithm (SNN), in which each sample data point votes for the points in its k-nearest neighborhood. Sets of points sh...
Conference Paper
A novel clustering algorithm CMune is presented for the purpose of finding clusters of arbitrary shapes, sizes and densities in high dimensional feature spaces. It can be considered as a variation of the Shared Nearest Neighbor algorithm (SNN), in which each sample data point votes for the points in its k-nearest neighborhood. Sets of points sharin...
Conference Paper
Full-text available
Clustering algorithms partition data objects into a certain number of clusters, where a cluster is described in terms of internal homogeneity and external separation. A new clustering algorithm CSHARP is presented for the purpose of finding clusters of arbitrary shapes and arbitrary densities in high dimensional feature spaces. It can be considered...

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Projects

Project (1)
Project
Research over algorithms that rely on density-peak technique specifically