Overview

The availability of large volumes of data and the generalized use of computer tools has transformed research and data analysis, orienting it towards certain specialized techniques encompassed under the generic name of Analytics that includes Multivariate Data Analysis (MDA), Data Mining and other Business Intelligence techniques.Data Mining can be defined as a process of discovering new and significant relationships, patterns and trends when examining large amounts of data. The techniques of Data Mining pursue the automatic discovery of the knowledge contained in the information stored in an orderly manner in large databases. These techniques aim to discover patterns, profiles and trends through the analysis of data using advanced statistical techniques of multivariate data analysis.The goal is to allow the researcher-analyst to find a useful solution to the problem raised through a better understanding of the existing data.Data Mining uses two types of techniques: predictive techniques, which trains a model on known input and output data so that it can predict future outputs, and descriptive techniques, which finds hidden patterns or intrinsic structures in input data.Descriptive techniques finds hidden patterns or intrinsic structures in data. It is used to draw inferences from datasets consisting of input data without labeled responses. Clustering is the most common descriptive technique. It is used for exploratory data analysis to find hidden patterns or groupings in data. Applications for clustering include gene sequence analysis, market research, and object recognition. This book develops classification descriptive techniques.

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