Introduce a New Algorithm for Data Clustering by Genetic Algorithm
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Authors
J. Vahidi
- Department of Applied Mathematics, Iran University of Science and Technology, Behshahr, Iran.
S. Mirpour
- Sama Technical and vocational training college, Islamic Azad University, Babol Branch, Babol, Iran.
Abstract
Clustering of data into adequate categories is one of the most important issues in pattern recognition. What is important in clustering, doing so is no predetermined pattern, provided that the same data should be in a category. In this paper, first, a clustering method using a grouping genetic algorithm (GGA) to describe, then the proposed model we introduce and the proposed method are tested on several sets of data and finally we compare the proposed method with the GGA algorithm.
The results show that the proposed algorithm is well-GGA gives us the answer and in terms of time and space complexity are much better than GGA.
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ISRP Style
J. Vahidi, S. Mirpour, Introduce a New Algorithm for Data Clustering by Genetic Algorithm, Journal of Mathematics and Computer Science, 10 (2014), no. 2, 144 - 156
AMA Style
Vahidi J., Mirpour S., Introduce a New Algorithm for Data Clustering by Genetic Algorithm. J Math Comput SCI-JM. (2014); 10(2):144 - 156
Chicago/Turabian Style
Vahidi, J., Mirpour, S.. "Introduce a New Algorithm for Data Clustering by Genetic Algorithm." Journal of Mathematics and Computer Science, 10, no. 2 (2014): 144 - 156
Keywords
- grouping genetic algorithm
- clustering
- pattern recognition
MSC
References
-
[1]
C. C. Aggarwal, J. Han, J. Wang, P. S. Yu, A framework for clustering evolving data streams, Proceedings of the 29th VLDB Conference, (2003)
-
[2]
L. E. Agustin-Blas, S. Salcedo-Sanz, S. Jimenez-Fernandez, L. Carro-Calvo, J. Del Ser , A new grouping genetic algorithm for clustering problems, Expert Systems with Applications , 39 (2012), 9695-9703.
-
[3]
Daniel Barbard, Requirements for Clustering Data Streams, ACM SIGKDD Explorations Newsletter, 3 (2002), 23-27
-
[4]
Jurgen Beringer, Eyke Hullermerier, Fuzzy Clustering of Parallel Data streams, Data & Knowledge Engineering , (2006), 180-204
-
[5]
Albert Bifet, Geo Holmes, Bernhard Pfahringer, Philipp Kranen, Hardy Kremer, Timm Jansen, Thomas Seidl , MOA: Massive Online Analysis, for Stream Classification and Clustering, , (2010)
-
[6]
Lior Cohen, Gil Avrahami, Mark Last, Abraham Kandel, Info-fuzzy algorithms for mining dynamic data streams, Applied Soft Computing, (2008), 1283-1294
-
[7]
M. M. Gaber, Arkady Zaslavsky, Shonali Krishnaswamy, Mining Data Streams: A Review, SIGMOD Record, vol. 34, no. 2 (2005)
-
[8]
Mohammad GhasemiGol, Hadi Sadoghi Yazdi, Reza Monsefi, A New Hierarchical Clustering Algorithm on Fuzzy Data (FHCA), International Journal of Computer and Electrical Engineering, vol. 2, no. 1 (2010)
-
[9]
S. Guha, R. Rastogi, K. Shim, CURE: An Efficient Clustering Algorithm for Large Databases, Proc. SIGMOD, (1998), 73-84
-
[10]
Richard J. Hathaway, James C. Bezdek, Extending Fuzzy and Probabilistic Clustering to Very Large Data Sets, Journal of Computational Statistics and Data Analysis, 51 (2006), 215-234
-
[11]
Madjid Khalilian, Norwati Mustapha, Data Stream Clustering, Challenges and Issues, (2010)
-
[12]
Raghu Krishnapuram, James M. Keller, A Possibilistic Approach to Clustering, IEEE TRANSACTIONS ON FUZZY SYSTEMS, vol. 1, no. 2 (1993)
-
[13]
Raghu Krishnapuram, James M. Keller, the Possibilistic C-Means Algorithm: Insights and Recommendations, IEEE TRANSACTIONS ON FUZZY SYSTEMS, vol. 4, no. 3 (1996)
-
[14]
Alireza Rezaei Mahdiraji, Clustering data streams: A survey of algorithms, International Journal of Knowledge-based and Intelligent Engineering Systems, (2009), 39-44
-
[15]
Mohamed Medhat Gaber, Arkady Zaslavsky, Shonali Krishnaswamy, Mining Data Streams: A Review, SIGMOD Record, vol. 34, no. 2 (2005)
-
[16]
D. P. Mercer, Linacre College, Clustering large datasets, , (2003)
-
[17]
Liadan O’Chalaghan, Nina Mishra, Adam Meyerson,Sudipto Guha, Rajeev Motwani, Streaming data algorithms for high quality clustering, Proc. of IEEE International Conference on Data Engineering, (2002), 685– 694
-
[18]
Nikhil R. Pal, Kuhu Pal, James M. Keller, James C. Bezdek, A Possibilistic Fuzzy c-Means Clustering Algorithm, IEEE Transactions on Fuzzy Systems, 517 - 530 (2005)
-
[19]
Renxia Wan, Xiaoya Yan, Xiaoke Su, A Weighted Fuzzy Clustering Algorithm for Data Stream, presented at ISECS International Colloquium on Computing, Communication, Control, and Management.CCCM ()
-
[20]
Xuanli Lisa Xie, Gerardo Beni, A Validity Measure for Fuzzy Clustering, , (1990)
-
[21]
T. Zhang, R. Ramakrishnan, M. Livny, BIRCH: An efficient data clustering method for very large databases, , ()