MATLAB data analysis and mining practices, matlab data analysis practices

MATLAB data analysis and mining practices, matlab data analysis practices
This is a high-quality pre-sale recommendation for database storage and management in computers, MATLAB data analysis and mining practices.
With more than 10 years of practical experience, many senior data mining experts explain the various technologies of Data Mining in depth.

It is recommended that you use more than 10 real cases to provide solutions for data mining in more than 10 industries, and provide relevant modeling files and source code.

Preface why write this book
LinkedIn analyzed the work experience and skills of more than 0.33 billion million users in the world and found that, among the currently most popular 25 skills, data mining talents ranked first. So what is data mining?
Data Mining is a process of mining hidden, unknown, and potentially valuable relationships, patterns, and trends from a large amount of data (including text, use these knowledge and rules to create a model for decision support and methods, tools, and processes that provide predictive decision support. Data Mining helps enterprises discover business trends, reveal known facts, and predict unknown results. Therefore, "Data Mining" has become a necessary way for enterprises to stay competitive.
However, compared with foreign countries, because of the low degree of informatization and incomplete internal information of enterprises, the retail industry, banking, insurance, securities and other industries are not ideal for data mining. However, as market competition intensifies, the willingness of various industries to use data mining technology is getting stronger and stronger. It can be predicted that in the next few years, data analysis applications in various industries will certainly develop from traditional statistical analysis to large-scale data mining applications. In the big data era, there is a shortage of data and talents. the cultivation of data mining professionals requires the accumulation of professional knowledge and professional experience. Therefore, this book focuses on the combination of Data Mining Theory and project case practices, allowing readers to gain a real data mining learning and practice environment, learn Data Mining knowledge faster and better, and accumulate professional experience.
In general, with the advent of the cloud era, big data technology will become increasingly important strategic significance. Big Data has penetrated into every industry and business function field and has gradually become an important production factor. The use of massive data predicts a new wave of productivity growth and consumer surge. Big Data analysis technology will help enterprise users to obtain, manage, process, and organize massive data within a reasonable period of time, and also provide positive help for enterprise business decision-making; as a cutting-edge technology in data storage and mining, big data analysis is widely used in strategic emerging industries such as IOT, cloud computing, and mobile Internet. Although big data is still in its infancy in China, its commercial value has been revealed, especially the big data analysis talents with practical experience have become a hot topic for enterprises. To meet the growing demand of big data analysis talents, many universities have begun to offer big data analysis courses of different degrees. As the core technology of the big data era, "Big Data Analysis" will surely become one of the important courses of mathematics and statistics majors in colleges and universities.
Featured books
Starting from practice, combining a large number of data mining engineering cases and teaching experience, the author gives an in-depth introduction to the relevant tasks in the data mining and modeling process, taking real cases as the main line: data exploration, data preprocessing, classification and prediction, clustering analysis, time series prediction, Association Rule Mining, intelligent recommendation, and deviation detection. Therefore, the arrangement of this book aims to solve the mining goal of an application. First, it introduces the background of the case and puts forward the mining goal, then describes the analysis method and process, and finally completes model construction, interspersed with Operation Training During modeling, embedding relevant knowledge points into the corresponding operation process. To help readers easily obtain a real lab environment, this book uses well-known MATLAB tools to process sample data for mining and modeling.
To facilitate your understanding of the case, this book provides authentic original sample data files and data exploration, data preprocessing, model building and evaluation, and other MATLAB code programs at different stages, readers can from the national university student data mining competition website (http://www.tipdm.org/ts/578.jhtml) free download. In addition, to meet the instructor's teaching needs, this book also provides process data files and PPT Courseware in the modeling stage, the program/model and related code of each stage of Data Mining Based on MATLAB, sas em, SPSS Modeler, R, TipDM, and other computer lab environments. You can call the hotline (40068-40020), enterprise QQ (4006840020) or the following public number TipDM (or Tip DataMining) consultation, you can also consult the relevant questions of this book through the above contact information.
Intended audience
University teachers and students who offer data mining courses.
At present, many colleges and universities in China have introduced data mining into undergraduate teaching. They have offered courses related to data mining technology in mathematics, computer science, automation, electronic information, finance, and other majors, however, the teaching of this course is still limited to theoretical introduction. Because the pure theoretical teaching is too abstract, it is often difficult for students to understand and the teaching effect is not ideal. The teaching based on practical cases and modeling practices provided in this book can enable teachers and students to give full play to interaction and creativity, link theory with practice, and achieve the best teaching effect.
Requirement Analysis and system design personnel.
On the basis of understanding the data mining principles and modeling process, use Data Mining cases to analyze and design data mining applications such as Precision Marketing, customer grouping, cross-sales, Loss Analysis, customer credit scores, fraud detection, and Intelligent Recommendation.
Data Mining developers.
On the basis of understanding the needs and design solutions of data mining applications, this type of personnel can quickly complete the programming implementation of data mining applications based on the third-party interfaces provided in this book.
Scientific research personnel who conduct data mining application research.
In order to better manage the scientific research work, many research institutes have developed their own scientific research business management systems and accumulated a large amount of scientific research information data during use. However, these scientific research business management systems do not conduct in-depth analysis on the data, and do not fully exploit the Hidden values of the data. Researchers need to use data mining modeling tools and related methodologies to explore the value of scientific research information, so as to improve the level of scientific research. Focus on advanced data analysis personnel.
Business reports and BI solutions may be useful for understanding past and present situations. However, the predictive analysis solution of data mining can also enable such personnel to capture future developments, so that their organizations can take the lead, rather than being passive. Because the predictive analysis solution of Data Mining applies complex statistical methods and machine learning technology to data, it uses predictive analysis technology to reveal hidden data in the transaction system or enterprise resource planning (ERP) the pattern and trend in the structure database and common files to provide a scientific basis for decision-making.
How to read this book
This book consists of 16 chapters, which are divided into three parts: basic articles, practice articles, and improvement articles. The basic section describes the basic principles of data mining. The practical section describes various real cases. Through in-depth analysis of the cases, readers can gain experience in data mining projects without knowing it, at the same time, we can quickly understand seemingly obscure data mining theories. In the course of reading, readers should make full use of the Case modeling data that is provided with books, and use relevant data mining and modeling tools to quickly understand relevant knowledge and theories through hands-on experiments.
. BASICS (1st ~ Chapter 5). Chapter 1st focuses on the basis of data mining. Chapter 2nd briefly describes the data mining and modeling tool MATLAB used in this book. Chapter 3rd ~ Chapter 5 describes the modeling process of data mining, including common algorithms and principles of data exploration, data preprocessing, and mining modeling.
Practice (6th ~ Chapter 15) focuses on the application of data mining technology in electric power, aviation, medical, Internet, manufacturing, and public services industries. In the case structure organization, this book first introduces the background and mining objectives of the case, then describes the analysis methods and processes, and finally completes the order of model construction. In the key aspect of the modeling process, interspersed with programs to implement code. Finally, we will deepen our understanding of data mining technology in case applications through hands-on practice.
This article introduces TipDM Data Mining and modeling tool, a Data Mining Application Based on MATLAB, this tool is used as an example to describe in detail the steps for completing Secondary Development of Data Mining Based on the MATLAB interface, so that readers can experience the charm of implementing Secondary Development of Data Mining Through MATLAB.
Errata and support
Except the cover signature, fan Zhe, Yun weibiao, Wang Lu, Xu yinggang, Jiang Yajun, Liao Xiaoxia, Li Bai Bing, Liu Mingjun, Liu Xiaoyong, Xue Yun, Hu Xiaohui, and Li Chenghua participated in the compilation of this book. liu Lijun, Xu baotong, Huang brilliant, Wang Yunfei, etc, due to the limited level of the author and the rush of writing time, some errors or inaccuracies may inevitably occur in the book, and readers are urged to criticize and correct it.
Readers can report errors and problems in the book to us through the public number or QQ number provided above. We will try our best to provide the most satisfactory answers to readers online. All modeling data files and source programs in this book can be downloaded from the National College Students' Data Mining competition website (www.tipdm.org). At the same time, we will release relevant content updates in a timely manner. If you have more valuable comments, you are welcome to send an email to the mailbox 13560356095@qq.com, look forward to your sincere feedback.
Thank you
In the course of writing this book, it was strongly supported by researchers from enterprises and institutions, to Guangdong Electric Power Research Institute, Guangxi Electric Power Research Institute, Guangdong Telecom Planning and Design Institute, Pearl River/Yellow Sea Fisheries Research Institute, Light Industry Environmental Protection Research Institute, South China Normal University, Guangdong University of Technology, Guangdong Normal University college, Nanjing University of Traditional Chinese Medicine, South China University of Technology, Hunan Normal University, Hanshan Normal University, Guangdong Institute of petrochemical technology, Sun Yat-sen University, Guangzhou Teddy smart Technology Co., Ltd., Wuhan Teddy smart Technology Co., Ltd. we would like to extend our deep gratitude to experts and teachers and students for their support.
In the process of editing and publishing this book, I also got the selfless help and support from many teachers and students who participated in the China Data Mining modeling competition (http://www.tipdm.org) and Yang fuchuan and Jiang Ying, the mechanical industry press, I would like to express my gratitude for this.
Zhang liangjun
This book is pre-sold by WAF website. Buy a portal and click the open link.