Clustering by Hybrid Canopy and PSO Algorithm for Environmental Issues Data
| Autor: | G. Vishnu Priya, J. Mary Sheela, A. Jaya Mabel Rani |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
Particle swarm optimization Critical area 02 engineering and technology computer.software_genre Water scarcity Environmental issue Water resources 020204 information systems 0202 electrical engineering electronic engineering information engineering Canopy clustering algorithm 020201 artificial intelligence & image processing Data mining Cluster analysis computer Global optimization |
| Zdroj: | 2018 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). |
| DOI: | 10.1109/iccic.2018.8782405 |
| Popis: | In today trendy world Environmental issues is a main critical area of study due to it's directly affects the human beings. Water scarcity is the main issue which we are facing now a days, which means lack of available water resources to meet the needs of water usage within an area. It previously affects every location around the world at least two to three months per year mainly during summer. Our concept mainly focuses on to predict water scarcity based on various district wise water available resources and amount of rainfall measures of past few years using data clustering based on Machine learning algorithms. Data clustering is the technique of extracting valuable information from the large collection of datasets. There are various types of machine learning clustering algorithms are available from local optimization to global optimization and hybrid of local and global optimization to get better optimum solution. This paper used hybrid canopy clustering algorithm and particle swarm optimization Technique to predict the water scarcity from the big environmental issue of water scarcity data about 800 data set with the analysis and implemented weka tool. |
| Databáze: | OpenAIRE |
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