Application and Analysis of K-Means Algorithms on a Decision Support Framework for Municipal Solid Waste Management

Many countries are facing the problem of sustainable solid waste management for a long time. The quantity of generating solid waste increase varies quickly as the rate of the population grows. Now, presently the government has focused on all waste segmentation levels like waste generated evolves and the technologies available to collect and process it. Many new advanced analytical techniques have been proposed for dealing with sustainable solid waste management problems. These advanced tools help the organizations to analyze municipal solid waste raw data, which is stored by local municipal bodies. We have used many innovative analytical tools and technology, such as data mining technology. On these analytical technologies, the clustering data classification technique is one of them. This paper focuses on K-mean clustering algorithms and their working in context with solid waste management. The analysis algorithms have been played an important role in decision making process. This type of algorithm helps the main decision-makers to make the right decisions or helps to improve the efficiency of the decision-making process. An excellent solid waste management framework must include all disturbing factors, including pollution generation, land use, energy use, financial costs, labor needs, monitoring, recycling rates, etc.