Data Mining: Concepts and TechniquesMorgan Kaufmann, 02.07.2022 - 752 Seiten Data Mining: Concepts and Techniques, Fourth Edition introduces concepts, principles, and methods for mining patterns, knowledge, and models from various kinds of data for diverse applications. Specifically, it delves into the processes for uncovering patterns and knowledge from massive collections of data, known as knowledge discovery from data, or KDD. It focuses on the feasibility, usefulness, effectiveness, and scalability of data mining techniques for large data sets. After an introduction to the concept of data mining, the authors explain the methods for preprocessing, characterizing, and warehousing data. They then partition the data mining methods into several major tasks, introducing concepts and methods for mining frequent patterns, associations, and correlations for large data sets; data classificcation and model construction; cluster analysis; and outlier detection. Concepts and methods for deep learning are systematically introduced as one chapter. Finally, the book covers the trends, applications, and research frontiers in data mining. - Presents a comprehensive new chapter on deep learning, including improving training of deep learning models, convolutional neural networks, recurrent neural networks, and graph neural networks - Addresses advanced topics in one dedicated chapter: data mining trends and research frontiers, including mining rich data types (text, spatiotemporal data, and graph/networks), data mining applications (such as sentiment analysis, truth discovery, and information propagattion), data mining methodologie and systems, and data mining and society - Provides a comprehensive, practical look at the concepts and techniques needed to get the most out of your data - Visit the author-hosted companion site, https://hanj.cs.illinois.edu/bk4/ for downloadable lecture slides and errata |
Inhalt
| 1 | |
| 23 | |
3 Data warehousing and online analytical processing | 85 |
basic concepts and methods | 145 |
advanced methods | 175 |
basic concepts and methods | 239 |
advanced methods | 307 |
basic concepts and methods | 379 |
10 Deep learning | 485 |
11 Outlier detection | 557 |
12 Data mining trends and research frontiers | 605 |
A Mathematical background | 655 |
Bibliography | 681 |
| 735 | |
Back Cover | 755 |
advanced methods | 431 |
Andere Ausgaben - Alle anzeigen
Data Mining: Concepts and Techniques Jiawei Han,Jian Pei,Hanghang Tong Keine Leseprobe verfügbar - 2022 |
Häufige Begriffe und Wortgruppen
aggregate algorithm applications approach arXiv association rules attributes autoencoder backpropagation Bayesian bicluster binary cells class label cluster analysis clustering methods compute Conf constraints convolution correlation corresponding cuboid data cube data lakes data mining data objects data set data tuples data warehouse database decision tree deep learning density dimensionality dimensionality reduction dimensions distance distribution efficient embedding error example feed-forward frequent itemsets frequent patterns function given gradient graph hidden hierarchical clustering high-dimensional data input data introduced iteration k-means kernel Knowledge Discovery large data layer linear logistic regression machine learning matrix measure minimum support multidimensional multiple negative neural networks node normal OLAP optimization output partitioning pattern mining prediction Proc pruning query random random forest regression represent sample Section selection semisupervised sequence sequential patterns similarity space statistical subset subspace supervised learning task techniques training tuples transaction transformation unit update values weight vector