A Neuro-Fuzzy Integrated Clustering for Weather Knowledge Analysis

Weather Information processing and knowledge extraction is one of the challenging applications of data mining. This process area requires authenticated and intelligent processing to obtain accurate information from the knowledge set. In this work, an intelligent clustering mechanism is defined to acquire such information. This neuro-fuzzy based model is applied on raw dataset defined with various weather characteristics including humidity, temperature, rainfall etc. The work is divided in three main stages. In first stage, the filtration over the dataset is performed to get more relevant information set. In second stage, the clustering is performed to divide the information set in knowledge groups. In final stage, the filtration over the knowledge set is performed to acquire the most effective knowledge. The results show the effective information analysis is obtained from the work.