Improved model-based clustering using evolutionary optimization
| Autor: | Kayvan Bijari, Emad Kebriaei, Hadi Zare |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
business.industry
Correlation clustering Constrained clustering Mixture model Machine learning computer.software_genre Determining the number of clusters in a data set ComputingMethodologies_PATTERNRECOGNITION CURE data clustering algorithm Expectation–maximization algorithm Canopy clustering algorithm Artificial intelligence business Cluster analysis computer Mathematics |
| Zdroj: | 2017 Artificial Intelligence and Robotics (IRANOPEN). |
| DOI: | 10.1109/rios.2017.7956464 |
| Popis: | As an optimization strategy Maximum Likelihood's definitive goal is to adjust a statistic model with a specific dataset, the method will adjust some variables of a statistical model from a dataset or a known distribution, so the model can “describe” each data sample and estimate the others. Clustering can be based on probability models to cover the missing values. This provides insights into when the data should conform to the model and led to development of new clustering methods such as Expectation Maximization (EM) which is based on the principle of Maximum Likelihood of unobserved variables in finite mixture models. Evolutionary Algorithms are trusted to further improve optimization tactics, in this paper Big-Bang Big-Crunch evolutionary algorithm have been used to boost model-based clustering and experimental results on the real datasets shows its superiority over the typical model-based clustering methods. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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