Data mining: the textbook Aggarwal, Charu C.
Publication details: Springer International Publishing 2015 SwitzerlandDescription: xxix, 734 pISBN:- 9783319141411
- 006.312 A4D2
| Item type | Current library | Collection | Call number | Status | Date due | Barcode | Item holds | |
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Ahmedabad General Stacks | Non-fiction | 006.312 A4D2 (Browse shelf(Opens below)) | Available | 190219 |
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| 006.31015195 A7C6 A computational approach to statistical learning | 006.31068403 K2M2 Machine learning for decision makers: cognitive computing fundamentals for better decision making | 006.310727 G6S8 Statistical machine learning: a unified framework | 006.312 A4D2 Data mining: the textbook | 006.312 A6D6 Doing data science in R: an introduction for social scientists | 006.312 B4M2 Machine learning for data streams: with practical examples in MOA | 006.312 C6 Contemporary issues in exploratory data mining in the behavioral sciences |
Table of contents:
1.Introduction to Data Mining
2.Data Preparation
3.Similarity and Distances
4.Association Pattern Mining
5.Association Pattern Mining: Advanced Concepts
6.Cluster Analysis
7.Cluster Analysis: Advanced Concepts
8.Outlier Analysis
9.Outlier Analysis: Advanced Concepts
10.Data Classification
11.Data Classification: Advanced Concepts
12.Mining Data Streams.- Mining Text Data
13.Mining Time-Series Data
14.Mining Discrete Sequences
15.Mining Spatial Data
16.Mining Graph Data
17.Mining Web Data
18.Social Network Analysis
19.Privacy-Preserving Data Mining
This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks. Until now, no single book has addressed all these topics in a comprehensive and integrated way. The chapters of this book fall into one of three categories:Fundamental chapters: Data mining has four main problems, which correspond to clustering, classification, association pattern mining, and outlier analysis. These chapters comprehensively discuss a wide variety of methods for these problems.Domain chapters: These chapters discuss the specific methods used for different domains of data such as text data, time-series data, sequence data, graph data, and spatial data.Application chapters: These chapters study important applications such as stream mining, Web mining, ranking, recommendations, social networks, and privacy preservation. The domain chapters also have an applied flavor.Appropriate for both introductory and advanced data mining courses, Data Mining: The Textbook balances mathematical details and intuition. It contains the necessary mathematical details for professors and researchers, but it is presented in a simple and intuitive style to improve accessibility for students and industrial practitioners (including those with a limited mathematical background). Numerous illustrations, examples, and exercises are included, with an emphasis on semantically interpretable examples. --publisher.
(http://www.springer.com/gp/book/9783319141411)
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