Mohamed Ali AbbasAlexandria University | AU · Department of Information Technology
Mohamed Ali Abbas
Doctor of Philosophy
Experienced Data Analyst with a demonstrated history of working in the government administration industry.
About
7
Publications
1,144
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49
Citations
Citations since 2017
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)
[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...
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...
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...
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...
Projects
Project (1)
Research over algorithms that rely on density-peak technique specifically
