Data Mining: Concepts and TechniquesElsevier, 9 ביוני 2011 - 744 עמודים Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. - Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects - Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields - Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data |
תוכן
| 1 | |
| 39 | |
Chapter 3 Data Preprocessing | 83 |
Chapter 4 Data Warehousing and Online Analytical Processing | 125 |
Chapter 5 Data Cube Technology | 187 |
Basic Concepts and Methods | 243 |
Chapter 7 Advanced Pattern Mining | 279 |
Basic Concepts | 327 |
Advanced Methods | 393 |
Basic Concepts and Methods | 443 |
Chapter 11 Advanced Cluster Analysis | 497 |
Chapter 12 Outlier Detection | 543 |
Chapter 13 Data Mining Trends and Research Frontiers | 585 |
| 633 | |
| 673 | |
מונחים וביטויים נפוצים
accuracy aggregate algorithm AllElectronics applications approach Apriori association rules base cuboid Bayesian bicluster cells Chapter class label classification cluster analysis clustering methods concept hierarchy Conf considered constraints contains context correlation count cube computation cuboid data analysis data cleaning data cube data marts data mining data objects data set data warehouse data warehousing decision tree defined density described dimensionality dimensions distance distribution efficient example Figure frequent itemsets function given graph high-dimensional data iceberg cube input k-means k-means algorithm k-medoids Knowledge Discovery large data Machine Learning measure minimum support mining process multidimensional data multiple neural network node normal OLAP online analytical processing outlier detection methods partitioning pattern mining prediction probabilistic Proc pruning query represents resulting sample scalable schema Section selection sequences similarity space statistical structure subset subspaces Suppose techniques training tuples transaction tuples typically values vector visualization