Abstract
Drastic climate change is one of the environmental concerns faced by all the living beings on earth today. So an efficient weather clustering should be done in order to better consolidate the climatic variations because the climatic predictions are environmental specific. Neo4j, one of the popular NoSQL databases, is used to represent the weather dataset in a graphical manner. The flexibility, scalability, and simplicity of the neo4j graph database help to represent the area-wise weather report in a simplified, graphical way and makes it convenient for the weather analysis. As Neo4j can handle complex and connected data, clustering the dataset by the k-means MSOINN based on Self-Organizing Incremental Neural Network (SOINN) algorithm, which uses the squared root of numerical, and categorical data enhances the accuracy and distribution contrast to the traditional k-means methodology.
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Lavanya, K., Kashyap, R., Anjana, S., Thasneen, S. (2020). An Enhanced K-Means MSOINN Based Clustering Over Neo4j with an Application to Weather Analysis. In: Singh Tomar, G., Chaudhari, N.S., Barbosa, J.L.V., Aghwariya, M.K. (eds) International Conference on Intelligent Computing and Smart Communication 2019. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-0633-8_43
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