ABSTRACT
The ever-evolving improvements in processing and storage capacity of gadgets demand effective data-mining procedures to obtain the correct and important data. Data summarization is an essential information investigation strategy, which is comprehensively characterized into two techniques, namely, semantics and syntactic. Clustering algorithms are utilized for semantic summarization, for example, fuzzy clustering algorithm. The proposed structure works in four phases and gives a considerable execution of different record plots. Using the Iris dataset for analysis, it is shown that the fuzzy clustering algorithm gives better results than K-means, fuzzy C-means, and the K-medoids algorithm. The execution of setup is done using U.C. Iarvin (UCI) machine-learning dataset. The exploratory outcomes exhibit that the planned system can unite the big information record prevalent as separated and already existing structures.
