Anzeige
Erschienen in:
2018 | OriginalPaper | Buchkapitel
Optimization of Fuzzy C-Means Algorithm Using Feature Selection Strategies
verfasst von : Kanika Maheshwari, Vivek Sharma
Erschienen in: Information Systems Design and Intelligent Applications
Verlag: Springer Singapore
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
In the era of Digital World, everything has a cost whether the cost is in terms of money, time, space, or data. Big data is a term used to define very large volume of data which possesses a lot of varieties in it. In the following paper, we are presenting feature selection strategy to optimize fuzzy-based clustering over very large data. By using selective features method, we can reduce number of features used to classify the following dataset; thus, the reduction of dimensions/features can help in optimizing iteration count, space, time as well as minimize objective function for the following dataset. In final observation, we found out the reduction in iteration count as well as time in comparison to literal fuzzy c-means algorithm that is 10.65 s and 9.84 s, respectively, for pen digits and cement dataset in comparison to PCA that is 8 s and 9 s and EFA that is 5.74 s and 8 s, respectively, for both the datasets.