Description
Data Mining is designed as a comprehensive reference for understanding the process of discovering knowledge from vast amounts of raw data. Data mining is a process that goes beyond simple database searching and is known as a combination of classical statistics, artificial intelligence, and machine learning. The book clearly explains how structured and unstructured data can be analyzed to extract hidden patterns and consumer behaviors for marketing and management purposes.
In different sections of this book, the five main steps of data mining are explained in detail, including determining business objectives, selecting data, preparing and cleaning data, modeling, and finally evaluating the results. The book places special emphasis on data management and reducing the dimensionality of models to prevent computational slowdown. It also teaches managers how to transform the results of predictive algorithms into practical strategies in the business world.
Book Features
- A step-by-step review of the standard data mining process from problem definition to results visualization.
- Explain the differences and applications of supervised (such as regression) and unsupervised (such as clustering) algorithms.
- Teaching data cleaning techniques and removing noise and outliers to optimize models.
- Analyze practical applications of data mining in diverse fields such as healthcare, finance, and e-commerce.
Book specifications
- Publisher: Springer
- Instructor/Author: Yong Yin
- Number of pages: 320
- Number of chapters: 14
- Format: PDF
Headlines
Pictures
User Guide
Extract the file and run it with the appropriate software.
Download link
File(s) password: www.downloadly.ir
File size
2.4 MB