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Clustering by a gen...
Clustering by a genetic algorithm with biased mutation operator
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- Auffarth, Benjamin (författare)
- Catalonia and Department of Electronicsl Engineering, University Autònoma de Barcelona, Spain
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2010
- 2010
- Engelska.
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Ingår i: 2010 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 1-8
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Abstract
Ämnesord
Stäng
- In this paper we propose a genetic al- gorithm that partitions data into a given number of clusters. The algorithm can use any cluster validity function as fitness function. Cluster validity is used as a criterion for cross-over operations. The cluster assignment for each point is accompanied by a tem- perature and points with low confidence are pref- erentially mutated. We present results applying this genetic algorithm to several UCI machine learning data sets and using several objective cluster validity functions for optimization. It is shown that given an appropriate criterion function, the algorithm is able to converge on good cluster partitions within few generations. Our main contributions are: 1. to present a genetic algorithm that is fast and able to converge on meaningful clusters for real-world data sets, 2. to define and compare several cluster validity criteria.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)
Nyckelord
- learning (artificial intelligence)
- pattern clustering
- UCI machine learning
- cluster validity function
- criterion function
- crossover operation
- fitness function
- genetic algorithm
- mutation operator
- optimization
- SRA - ICT
- SRA - Informations- och kommunikationsteknik
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)