Application of fuzzy clustering of massive scattered point cloud data in English vocabulary analysis

摘要

Cloud computing is used to find useful information in large amounts of data. The cloud calculates techniques to enable analysis and English vocabulary analysis to solve the types of research problems. This article describes the analysis of large-scale scattering point cloud data by fuzzy clustering of English vocabulary. English vocabulary analysis is digging into information from a file of English words in the text. It is also called intelligent Knowledge Discovery (KDT). Search for pattern vocabulary information, and extracts from structured and unstructured data, and fuzzy clustering. Starting from the famous fuzzy clustering, which is mainly used for pellets, some methods combine cluster-specific large-scale Massive Scattered in Vocabulary Data Analysis. The cloud-based English vocabulary set is divided into functionally three steps. Preprocessing is based on the first steps of text that give rise to techniques for demising. Before classification, the feature is selected using the Weight-Based Feature Selection (WFS) method. Fuzzy Clustering is used to improve the performance of functional classification by selection based on the proposed classification. It is a classification that is only useful to facilitate an effective classification process for English vocabulary datasets. Of these, Fuzzy Clustering improves classification accuracy and reduces error rates and classification times. The proposed system improves the accuracy of the analysis and reduces the complexity of time.