Data Mining, Southeast Asia EditionOur ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge. Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data— including stream data, sequence data, graph structured data, social network data, and multi-relational data.
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Results 1-5 of 59
... Vector Machines 337 6.7.1 The Case When the Data Are Linearly Separable 337 6.7.2 The Case When the Data Are Linearly Inseparable 342 Associative Classification: Classification by Association Rule Analysis 344 Lazy Learners (or Learning ...
... Vector Objects 397 A Categorization of Major Clustering Methods 398 Partitioning Methods 401 7.4.1 Classical Partitioning Methods: k-Means and k-Medoids 402 7.4.2 Partitioning Methods in Large Databases: From k-Medoids to CLARANS 407 ...
... vector machines, associative classification, k-nearest neighbor classifiers, case-based reasoning, genetic algorithms, rough set theory, and fuzzy set approaches. Methods of regression are introduced. Issues regarding accuracy and how ...
... vector format, where roads, bridges, buildings, and lakes are represented as unions or overlays of basic geometric constructs, such as points, lines, polygons, and the partitions and networks formed by these components. Geographic ...
... vector machines, and k-nearest neighbor classification. Whereas classification predicts categorical (discrete, unordered) labels, prediction models continuous-valued functions. That is, it is used to predict missing or unavailable ...
Contents
1 | |
47 | |
105 | |
4 Data Cube Computation and Data Generalization | 157 |
5 Mining Frequent Patterns Associations and Correlations | 227 |
6 Classification and Prediction | 285 |
7 Cluster Analysis | 383 |
8 Mining Stream TimeSeries and Sequence Data | 467 |
9 Graph Mining Social Network Analysis and Multirelational Data Mining | 535 |
10 Mining Object Spatial Multimedia Text and Web Data | 591 |
11 Applications and Trends in Data Mining | 649 |
An Introduction to Microsofts OLE DB for Data Mining | 691 |
Bibliography | 703 |
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Geographic Data Mining and Knowledge Discovery Harvey J. Miller,Jiawei Han No preview available - 2003 |