@article {10.3844/jcssp.2014.1269.1280,
article_type = {journal},
title = {CLUSTERING TWEETS USING CELLULAR GENETIC ALGORITHM},
author = {Adel, Amr and ElFakharany, Essam and Badr, Amr},
volume = {10},
number = {7},
year = {2014},
month = {Jun},
pages = {1269-1280},
doi = {10.3844/jcssp.2014.1269.1280},
url = {https://thescipub.com/abstract/jcssp.2014.1269.1280},
abstract = {As the popularity of Twitter continues to increase rapidly, it is extremely necessary to analyze the huge amount of data that Twitter users generate. A popular method of tweet analysis is clustering. Because most tweets are textual, this study focuses on clustering tweets based on their textual content similarity. This study presents tweet clustering using cellular genetic algorithm cGA. The results obtained by cGA are compared with those obtained by generational genetic algorithm in terms of average fitness, average time required for execution and number of generations. Experimental results are tested with two sets: One of 1000 tweets and the second formed of 5000 tweets. The results show a nearly equal performance for both algorithms in terms of the average fitness of the solution. On the other hand, cGA shows a much faster performance than generational. These results demonstrate that cellular genetic algorithm outperforms generational genetic algorithm in tweet clustering.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}