This is a computer database storage and management class of high-quality pre-sale recommendation "MATLAB data Analysis and mining actual combat".
A number of senior data mining experts more than 10 years of practical experience crystallization, in-depth interpretation of the various aspects of data mining technology.
Editor's recommendation More than 10 real-world cases provide solutions for data mining in over 10 industries and provide relevant modeling files and source code.
Foreword PartWhy did you write this book ?
following an analysis of the work experience and skills of more than 330 million users worldwide, LinkedIn ranked first among the 25 most sought-after skills for data mining talent. So what is data mining?
Data Mining is the mining of hidden, previously unknown, potentially valuable relationships, patterns, and trends from large amounts of data (including text), using these knowledge and rules to build models for decision support, and methods, tools, and processes that provide predictive decision support. Data mining helps businesses discover trends, uncover known facts, and predict unknown results, so "data mining" has become a necessary way for businesses to remain competitive.
However, compared with foreign countries, because of the low level of informatization, the internal information is not complete, so the retail industry, banking, insurance, securities and other industries in the application of data mining is not ideal. But as the market competition intensifies, the willingness of various industries to data mining technology is more and more strong, it can be expected that in the next few years, the application of data analysis in various industries will be developed from traditional statistical analysis to large-scale data mining applications. In the era of big data, the shortage of data and talent, the cultivation of data mining professionals requires the accumulation of professional knowledge and professional experience. Therefore, this book pays attention to the combination of data mining theory and project case practice, can let the reader get real data mining learning and practice environment, faster, better learning data mining knowledge and accumulate professional experience.
In general, with the advent of the cloud era, big data technology will have more and more important strategic significance. Big data has penetrated into every industry and business function, becoming an important factor of production, and the use of massive amounts of data heralds a new wave of productivity growth and consumer surge. Big data analysis technology will help enterprise users to seize, manage, process, tidy up large amounts of data in a reasonable time, but also to provide positive help for business decision-making; Big data analytics, as a leading technology for data storage and analysis, is widely used in strategic industries such as IoT, cloud computing, mobile internet, etc. While big data is still at an early stage in the country, its commercial value has emerged, especially for big data analysts with practical experience. To meet the needs of the growing number of big data analysts, many universities have begun experimenting with different levels of big data analytics courses. As the core technology of the Big Data era, "Big Data analysis" will become one of the important courses of mathematics and statistics in universities.
featured in this book
the author from the practice, combined with a large number of data mining engineering cases and teaching experience, taking the real case as the main line, in a brief introduction to the data mining modeling process related tasks: Data exploration, data preprocessing, classification and prediction, cluster analysis, time series prediction, association rules Mining, Intelligent recommendation, deviation detection and so on. Therefore, the layout of this book is to solve the mining objectives of an application premise, first introduced the case background and proposed the excavation target, then elaborated the analysis method and the process, finally completes the model construction, in introduces the modelling the process interspersed the operation training, the relevant knowledge point embeds the corresponding operation process. To make it easy for readers to get a realistic experimental environment, the book uses the familiar MATLAB tools to process sample data for mining modeling.
in order to facilitate the reader's understanding of the case, this book provides the actual raw sample data files and data exploration, data preprocessing, model building and evaluation of the various stages of MATLAB code program, readers can from the National University of Data Mining competition website (http://www.tipdm.org/ ts/578.jhtml) free download. In addition, in order to meet the needs of teachers to teach, this book also specifically provides the modeling stage of the process data files, PPT Courseware, and based on Matlab, SAS EM, SPSS Modeler, R, TIPDM and other computer experiments under the environment of the data mining procedures/models and related code, The reader can be consulted through the hotline (40068-40020), the Enterprise QQ (4006840020) or the following public number TIPDM (or tip datamining), and can also consult the relevant questions of this book through the above contact information.
This book applies to the object
The university teachers and students who open the data mining course.
At present, many universities in China have introduced data mining into undergraduate teaching, and have set up courses related to data mining technology in mathematics, computer, automation, electronic information, finance and so on, but the teaching of this course is still mainly limited to theoretical introduction. Because the simple theory teaching is too abstract, the students often understand difficult, the teaching effect is not very ideal. This book provides teaching based on practical cases and modeling practice, which can make the teachers and students fully exert their interactivity and creativity, and integrate theory with practice to achieve the best teaching effect.
Requirements analysis and system design staff.
This type of personnel can be based on understanding the principles of data mining and modeling process, combined with data mining cases to complete accurate marketing, customer clustering, cross-selling, loss analysis, customer credit scoring, fraud detection, intelligent recommendations and other data mining applications, such as the needs of analysis and design.
Data mining developers.
This type of personnel can be used to understand data mining application requirements and design solutions, combined with the third-party interface provided by this book to quickly complete the application of data mining programming implementation.
A researcher who carries out research on data mining applications.
In order to better manage the scientific research work, many scientific research institutes have developed their own scientific research business management system, and accumulated a large amount of scientific research information data in the process of using them. However, these research business management systems generally do not carry out in-depth analysis of these data, and do not have the hidden value of data to fully exploit. Researchers need to use data mining modeling tools and related methodologies to dig deep into the value of scientific research information, so as to improve the level of scientific research. People who are interested in advanced data analytics.
Business reporting and business intelligence solutions can be very useful for understanding past and present situations. However, predictive analytics solutions for data mining can also enable such people to anticipate future developments and allow their institutions to preempt, not be passive. Because the predictive analytics solution for data mining applies complex statistical methods and machine learning techniques to data, it provides a scientific basis for decision making by using predictive analytics to uncover patterns and trends hidden in trading systems or enterprise resource planning (ERP), structural databases, and common documents.
How to read this book
The book is a total of 16 chapters, divided into three articles: basic, practical and improve the article. The basic article introduces the basic principles of data mining; the actual cases are introduced, through the analysis of the case, so that the readers unknowingly get the experience of data mining projects, and quickly understand the seemingly difficult data mining theory. In the process of reading, readers should make full use of the case modeling data accompanying the book, and through the computer experiments, through the relevant data mining modeling tools, to quickly understand the relevant knowledge and theory.
. The basic chapter (1th to 5th), the 1th chapter of the main content is the basis of data mining, the 2nd chapter of the Data Mining modeling tool used in this book is a concise description of MATLAB, the 3rd to 5th chapter introduces the modeling process of data mining, including data exploration, data preprocessing and mining modeling common algorithms and principles.
Actual combat (the 6th to 15th chapter), focus on the data mining technology in the power, aviation, medical, Internet, manufacturing and public services and other industries to analyze the application. In the case structure organization, this book is based on the first introduction of the case background and the excavation target, then elaborated the analysis method and the process, finally completes the model construction sequence carries on, the modeling process key link, interspersed the procedure to realize the code. Finally, through the practice of computer, deepen the understanding of data mining technology in case application.
In the 16th chapter, this paper introduces the data mining application software--TIPDM based on Matlab two times development, and takes this tool as an example, it introduces the steps of data mining two development based on MATLAB interface. Enable readers to experience the powerful charm of two of data mining development through MATLAB.
Errata and Support
In addition to the signature of the cover, Zangjie, Yun Weipeo, Wang Lu, Xu Yinggang, Jiang Yaqun, Liao, Li Baibing, Liu Nanjun, Liu Xiaoyong, Shiyun, Hu Xiaohui, Li Chenghua, Liu Lijun, Xu Baotong, Huang Brilliance, Yun, etc., are also involved in the writing of the book. There will inevitably be some errors or inaccuracies in the book, and the reader is urged to criticize.
Readers can use the errors in the book and the problems encountered in the previous provided by the public number or QQ number feedback to us, we will try to provide readers on-line the most satisfactory answer. This book's full modeling data file and the source program, can be downloaded from the National University student Data Mining Competition website (www.tipdm.org), and we will release the corresponding content update in time. If you have more valuable comments, welcome to email [email protected], looking forward to receive your sincere feedback.
Thanks
In the process of writing, the majority of enterprises and institutions have been strongly supported by scientific research personnel, in this I would like to Guangdong Electric Power Research Institute, Guangxi Electric Power Research Institute, Guangdong Telecom Planning and Design Institute, Zhujiang/HUANGHAI Aquatic Research Institute, light Industry Environmental Protection Research Institute, South China Normal University, Guangdong University of Technology, Guangdong technical Teachers College, Nanjing University of Traditional Chinese Medicine, South China University of Science and Technology, Hunan Normal University, Hanshan teachers ' College, Guangdong Petrochemical Institute, Zhongshan University, Guangzhou, Teddy Intelligent Technology Co., Ltd., Wuhan, and other units to give support to the experts and teachers and students to extend their deep gratitude.
In the process of editing and publishing of this book, we are also grateful for the selfless help and support of many teachers and students in China Data Mining Modelling Competition (http://www.tipdm.org), Yang Fuchuan and Jiang Ying of mechanical industry press, and others.
Zhang Liang are
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MATLAB data analysis and mining actual combat
