Exploring data by PCA and k-means for IEEE Xplore digital library

Abstract

An important feature in data analysis is the exploration and data representation. This article describes the Principal Components Analysis techniques (PCA) and clusters analysis with k-means, in order to represent a set of two-dimensional spatial data and group similar data to find relationships between the two techniques. Data is extracted from IEEE Xplore digital library, which lacks processing tools and information display since it doesn't permit analysis and identification of trends and patterns in a query. At the end of the article, is discussed as a technique of data analysis unsupervised allows grouping and organizing of data by proximity based on the variance, finding similar keywords between groups and major components, allowing temporary and evolutionary view of a set of keywords, which can later be interpreted as topics and areas of exploration and research.