IEEE Style Citation: Neha Sharma, Pawan Makhija, “A Review on Optimizing Clustering Technique for Data Stream using Genetic Algorithm,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.635-637, 2018.
MLA Style Citation: Neha Sharma, Pawan Makhija "A Review on Optimizing Clustering Technique for Data Stream using Genetic Algorithm." International Journal of Computer Sciences and Engineering 6.9 (2018): 635-637.
APA Style Citation: Neha Sharma, Pawan Makhija, (2018). A Review on Optimizing Clustering Technique for Data Stream using Genetic Algorithm. International Journal of Computer Sciences and Engineering, 6(9), 635-637.
BibTex Style Citation:
@article{Sharma_2018,
author = {Neha Sharma, Pawan Makhija},
title = {A Review on Optimizing Clustering Technique for Data Stream using Genetic Algorithm},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {9 2018},
volume = {6},
Issue = {9},
month = {9},
year = {2018},
issn = {2347-2693},
pages = {635-637},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=2919},
doi = {https://doi.org/10.26438/ijcse/v6i9.635637}
publisher = {IJCSE, Indore, INDIA},
}
RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i9.635637}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=2919
TI - A Review on Optimizing Clustering Technique for Data Stream using Genetic Algorithm
T2 - International Journal of Computer Sciences and Engineering
AU - Neha Sharma, Pawan Makhija
PY - 2018
DA - 2018/09/30
PB - IJCSE, Indore, INDIA
SP - 635-637
IS - 9
VL - 6
SN - 2347-2693
ER -
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Abstract
In the current world , various sources like sensors, social media, web logs, network monitoring devices, traffic monitoring devices are generating lots of data. This huge data is arriving continuously, with high speed and changing its nature with time. Extracting useful information from the data stream demands enhancement in existing technologies of Data Mining. Clustering is an important part of data mining in which similar data points are merge into one group. Use of genetic algorithm in clustering data stream is an emerging technology. In this paper, we are discussing clustering techniques for data stream using Genetic Algorithm.
Key-Words / Index Term
Data Stream, Genetic Algorithms, Clustering
References
[1] Gaber, Mohamed Medhat, Arkady Zaslavsky, and Shonali Krishnaswamy. "Mining data streams: a review." ACM Sigmod Record 34.2 (2005): 18-26.
[2] Mahdiraji, Alireza Rezaei. "Clustering data stream: A survey of algorithms." International Journal of Knowledge-based and Intelligent Engineering Systems 13.2 (2009): 39-44.
[3] Gao, Ming-ming, Chang Tai-hua, and Xiang-xiang Gao. "Application of Gaussian mixture model genetic algorithm in data stream clustering analysis."Intelligent Computing and Intelligent Systems (ICIS), 2010 IEEE International Conference on. Vol. 3. IEEE, 2010.
[4] Zhou, Aoying, et al. "Distributed data stream clustering: A fast EM-based approach." Data Engineering, 2007. ICDE 2007. IEEE 23rd International Conference on. IEEE, 2007.
[5] Heng, Liang. "Fast Clustering Optimization Method of Large-Scale Online Data Flow Based on Evolution Incentive."2014 Fifth International Conference on Intelligent Systems Design and Engineering Applications (ISDEA). IEEE, 2014.
[6] Alsayat, Ahmed, and Hoda El-Sayed. "Social media analysis using optimized K-Means clustering." Software Engineering Research, Management and Applications (SERA), 2016 IEEE 14th International Conference on. IEEE, 2016.
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