New Internet: Big Data Mining

Author: new Internet: Big Data Mining Author: Tan Lei [Translator's introduction] Press: Electronic Industry Press ISBN: 9787121196706 Release Date: March 2013: 16 open pages: 376 versions: 1-1 category: Computer> database storage and management
More about new Internet: Big Data Mining computer books new Internet: big Data Mining comprehensively introduces how to use data mining technology to extract and generate business knowledge from massive data of various structures (databases) or non-structured (Web. The author combs various common data mining algorithms and information collection technologies, systematically describes how to perform data mining on Internet log analysis, email marketing, Internet advertising, and e-commerce in actual applications, this section focuses on the principles and algorithms of Data Mining in the application of massive data mining on the Internet. New Internet: Big Data Mining main features: a comprehensive introduction to the basic concepts and technologies of data mining and big data; a large number of practical cases, practical; this section describes in detail the latest commercial applications in the Big Data Mining Field. New Internet: Big Data Mining is an ideal reference for Data Mining Research and Development, or data operation professionals in Internet-related industries, you can also get started with data mining applications. Directory new Internet: chapter 1 Introduction of big data mining-from the gold rush to the mining master 1 1st in the big data era "Four v" 2 1.1 What is Big Data Mining 5 1.2.1 from data analysis to Data Mining 6 1.2.2 Web Mining 9 1.2.3 Big Data Mining-"big" 10 1.3 Domestic and International Development of big data mining 12 1.3.1 Application Development of Data Mining 12 1.3.2 Research and Development of Data Mining 17 1.4 contents 19 Chapter 2nd one hour learn about Data Mining 23 2.1 how data mining solves the problem 23 2.1.1 diapers and beer 23 2.1.2 target and pregnancy prediction index 24 2.1.3 e-commerce website traffic analysis 25 2.2 Classification: from the perspective of face recognition system 27 2.2.1 Classification Algorithm Application 29 2.2.2 Data Mining Classification Technology 33 2.2.3 classification algorithm evaluation 37. 2.3 All for business 40 2.3.1 what is business intelligence) 40 2.3.2 nine laws of Data Mining 43 2.4 data mining is very tangled 44 2.5 basic procedures of Data Mining 45 2.5.1 General steps of Data Mining 45 2.5.2 common concepts in Data Mining 47 2.5.3 crisp- DM 51 2.5.4 evaluation of data mining 53 2.5.5 knowledge of data mining results 55 2.6 resources in this Chapter 59 chapter 3rd data warehouse-the cornerstone of Data Mining 60 3.1 Data Warehouse 60 3.1.1 Data Warehouse definition 61 3.1.2 Data Warehouse and database 63 3.2 introduction to traditional data warehouse 64 3.3 basic structure of Data Warehouse 67 3.4 OLAP Online Analytical Processing 69 3.5 data warehouse on cloud storage 71 3.5.1 by Google cloud architecture 71 3.5.2 Open-Source Distributed System hadoop 77 3.5.3 Facebook data warehouse 85 3.5.4 nosql 86 3.6 related resources in this Chapter 89 Chapter 4th data mining algorithms and principles 91 4.1 data mining algorithms 91 4.2 Data mining 10 classic algorithms 92 4.3 classification algorithms (classification) 96. 4.4 clustering algorithm (clustering) 99 4.5 association algorithm 102 4.5.1 concepts in Association Algorithms 103 4.5.2 Association Rule Data Mining Process 105 4.5.3 classification of association rules 106 4.5.4 execution example of the Apriori algorithm 107 4.5.5 research and optimization of association rule mining algorithms 108 sequence Mining) 113 4.7 Data Mining Modeling Language pmml 115 4.8 related resources in this chapter 117 chapter 5th before data mining 120 5.1 data integration 121 5.2 why data preprocessing 122 5.3 124 data preprocessing 124 5.3.1 data cleaning 5.3.2 data conversion 129 5.3.3 data statute 132 5.4 related resources in this Chapter 134 chapter 6th characteristics of R language and other data mining tools 136 6.1 history of R language 136 6.1.1 R language 142 6.1.2 R language and Data Mining 149 6.2 Other data mining tools 152 6.2.1 MATLAB 153 6.2.2 other commercial data mining tools 155 6.2.3 open source data mining tools WEKA 159 6.3 Data Mining and cloud 160 6.4 related resources in this chapter 162 chapter 7th Internet logs analysis 164 7.1 website log introduction 165 7.2 website log processing 175 7.2.1 web log preprocessing 175 7.2.2 web log analysis and data mining 181 7.3 Mail Log 183 7.4 related resources in this chapter 184 chapter 8th Data Mining and electronics email 186 8.1 email marketing and spam filtering 186 8.2 Data Mining and email marketing 189 8.2.1 how to effectively conduct email marketing 189 8.2.2 email marketing case sharing one of 195 8.2.3 email marketing case sharing 2 200 8.2.4 use Data Mining RFM model to improve mail marketing effectiveness 203 8.3 Data Mining and spam filtering 208 8.3.1 spam 209 8.3.2 spam filtering technology 209 8.3.3 spam filtering case 215 8.4 related resources in this chapter 218 9th chapter Data Mining and Internet advertisement 219 9.1 Internet advertisement 219 9.2 advertisement cheating behavior 223 9.3 website Alliance advertisement 225 9.4 Data Mining on website Alliance advertisement 226 9.4.1 data helps Network Alliance advertisement 227 9.4.2 how to deal with network alliance advertisement cheating 236 9.5 related resources in this Chapter 241 Chapter 10th Data Mining and E-Commerce 242 10.1 Current Situation of e-commerce in China 242 10.2 selling rice on the Internet 248 using data to Master customers 10.3 10.3.1 when customers come, from where to 253 10.3.2 what is the customer's favorite product 257 10.3.3 competition and anti-competition analysis 260 10.3.4 what else will the customer buy 261 10.3.5 what customers are what we need 264 10.4 e-commerce case 265 10.4.1 Electronics business Enterprise case 1 266 10.4.2 e-commerce enterprise case 2 279 10.5 related resources in this chapter 286 chapter 11th Data Mining and Web Mining 288 11.1 Personalization on the Internet-like 289 11.1.1 like = like 289 11.1.2 like = like 290 11.2 Web Mining and SNS 295 11.2.1 SNS data value 295 11.2.2 SNS data associations 297 11.2.3 SNS user relations 299 11.3 Data Mining and privacy 302 11.4 related to this chapter resource 307 chapter 12th Data Mining and mobile Internet 308 12.1 particularity of mobile Internet 308 12.1.1 locking user data value 309 12.1.2 forms of mobile Internet Data 310 12.1.3 value of mobile Internet Geographic location information 312 12.2 Data Mining and LBS 314 12.2.1 using Pu Learning Algorithms for Text Mining 315 12.2.2 using similarity matching algorithms for location mining 318 12.3 problems faced by mobile Internet Data 320 12.4 related resources in this Chapter 322 Appendix A Technology vocabulary 323 appendix B English reference table 335 Appendix C Chinese Reference Table 347 appendix D Weibo 350 Appendix E blog and other websites 351 source of this book information: china Interactive publishing network