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Your search keyword '"Hedar, Abdel-Rahman"' showing total 3 results
Start Over Author "Hedar, Abdel-Rahman" Topic data mining
3 results on '"Hedar, Abdel-Rahman"'

1. A rough set approach to feature selection based on scatter search metaheuristic

Author

M. Ibrahim Abdel-Monem, Jue Wang, Hedar Abdel-Rahman, and Qi Zhang

Subjects
business.industry, Heuristic (computer science), Pattern recognition, Feature selection, computer.software_genre, Tabu search, Reduction (complexity), Simulated annealing, Genetic algorithm, Computer Science (miscellaneous), Artificial intelligence, Rough set, Data mining, business, Metaheuristic, computer, Information Systems, Mathematics
Abstract

Rough set theory is an effective method to feature selection, which has recently fascinated many researchers. The essence of rough set approach to feature selection is to find a subset of the original features. It is, however, an NP-hard problem finding a minimal subset of the features, and it is necessary to investigate effective and efficient heuristic algorithms. This paper presents a novel rough set approach to feature selection based on scatter search metaheuristic. The proposed method, called scatter search rough set attribute reduction (SSAR), is illustrated by 13 well known datasets from UCI machine learning repository. The proposed heuristic strategy is compared with typical attribute reduction methods including genetic algorithm, ant colony, simulated annealing, and Tabu search. Computational results demonstrate that our algorithm can provide efficient solution to find a minimal subset of the features and show promising and competitive performance on the considered datasets.

Published
2014
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2. MINING SOCIAL NETWORKS' ARABIC SLANG COMMENTS.

Author

Soliman, Taysir Hassan A., Elmasry, Mostafa A., Hedar, Abdel Rahman, and Doss, M. M.

Subjects
ONLINE social networks, DATA mining, SENTIMENT analysis, SUPPORT vector machines, ARABIC language
Abstract

Social networks have affected the way the new generation think all over the world, specifically in the Middle East. Their effectiveness appears in the revolutions of Tunisia, Egypt, and Syria. Therefore, social networks opinion mining for Arabic slang language has become an essential since it is widely used between the youth generation. Arabic slang language suffers from two main problems, which are the new expressive (opinion) words and idioms as well as the unstructured format. Mining Arabic slang language requires efficient techniques to extract youth opinions on various issues, such as news websites. In this paper, we propose a SVM-based classifier for Arabic slang language, applying sentiment analysis, to classify youth news' comments on Facebook. This classifier consists of three main phases: 1) Arabic comments' data preparation, 2) Data preprocessing, and 3) data classification. In addition, a Slang Sentimental Words and Idioms Lexicon (SSWIL) of opinion words is built, used by Arab youth in their comments on news topics, Facebook1 posts and comments, twits in Twitter2 and reviews. This paper works on users' comments and SSWIL enhances the classification task to be 86.86% of classified comments instead of 75.35% when using classical opinion words lexicon with precision 88.63 and recall 78 instead of 82.4 and 59.33 respectively. [ABSTRACT FROM AUTHOR]

Published
2013

3. K-Means Cloning: Adaptive Spherical K-Means Clustering.

Author

Sewisy, Adel A., Hedar, Abdel-Rahman, Abdel-Hakim, Alaa E., and Ibrahim, Abdel-Monem M.

Subjects
*K-means clustering, *CLONING, *ALGORITHMS, *DATA mining, *SIMULATED annealing
Abstract

We propose a novel method for adaptive K-means clustering. The proposed method overcomes the problems of the traditional K-means algorithm. Specifically, the proposed method does not require prior knowledge of the number of clusters. Additionally, the initial identification of the cluster elements has no negative impact on the final generated clusters. Inspired by cell cloning in microorganism cultures, each added data sample causes the existing cluster 'colonies' to evaluate, with the other clusters, various merging or splitting actions in order for reaching the optimum cluster set. The proposed algorithm is adequate for clustering data in isolated or overlapped compact spherical clusters. Experimental results support the effectiveness of this clustering algorithm. [ABSTRACT FROM AUTHOR]

Published
2018
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