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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From inside the book
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Page 114
The schema graph resembles a starburst, with the dimension tables displayed in
a radial pattern around the central fact table. ... Theschemacontains a central fact
table for sales that contains keys to each of the four dimensions, along with two ...
The schema graph resembles a starburst, with the dimension tables displayed in
a radial pattern around the central fact table. ... Theschemacontains a central fact
table for sales that contains keys to each of the four dimensions, along with two ...
Page 115
Figure 3.4 Example 3.2 time dimension table sales fact table item dimension
table time_ key time_key item_key day item_key item_name day_of_the_week
branch_key brand month location_key type quarter dollars_sold supplier_type
year ...
Figure 3.4 Example 3.2 time dimension table sales fact table item dimension
table time_ key time_key item_key day item_key item_name day_of_the_week
branch_key brand month location_key type quarter dollars_sold supplier_type
year ...
Page 116
Figure 3.5 Example 3.3 time dimension table time_key day day_of_week month
quarter year supplier dimension table supplier_key supplier_type sales fact table
time_key item_key branch_key location_key dollars_sold units_sold item ...
Figure 3.5 Example 3.3 time dimension table time_key day day_of_week month
quarter year supplier dimension table supplier_key supplier_type sales fact table
time_key item_key branch_key location_key dollars_sold units_sold item ...
Page 117
Figure 3.6 3.2.3 Example 3.4 time sales item shipping shipper dimension table
time_key fact table time_key dimension table item_key fact table item_key
dimension table shipper_key day item_key item_name time_key shipper_name ...
Figure 3.6 3.2.3 Example 3.4 time sales item shipping shipper dimension table
time_key fact table time_key dimension table item_key fact table item_key
dimension table shipper_key day item_key item_name time_key shipper_name ...
Page 118
Example 3.5 Example 3.6 define dimension time as (time key, day, day of week,
month, quarter, year) define dimension item as (item key, ... Definingsupplierin
this way implicitly creates asupplier keyin the item dimension table definition.
Example 3.5 Example 3.6 define dimension time as (time key, day, day of week,
month, quarter, year) define dimension item as (item key, ... Definingsupplierin
this way implicitly creates asupplier keyin the item dimension table definition.
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This is a good book. Lot of thinking work is needed to read such books. As they say....its all in your head.
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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 |
Other editions - View all
Common terms and phrases
accuracy aggregate algorithm AllElectronics applications approach Apriori association rules attribute Bayesian categorical cells Chapter class label classification cluster analysis clustering methods concept hierarchy constraints contains correlation count crosstab cuboid data analysis data cube data marts data mining system data set data streams data warehouse database systems decision tree defined described dimension table dimensions distribution document efficient example Figure frequent itemsets frequent patterns function given graph graph mining iceberg cube input k-medoids machine learning measure minimum support mining process multidimensional multimedia data multiple multirelational neural network node objects OLAP on-line outliers partition pattern mining performed prediction pruning regression relational database represented retrieval ROLAP scalable schema Section selection sequence similar snowflake schema space spatial data specified statistical stored stream data structure subset Suppose target techniques tion training tuples transaction transformation tuples typically values variables vector visualization
Popular passages
Page 230 - More formally, let / = (ih i'2 im) be a set of literals, called items. Let D be a set of transactions, where each transaction T is a set of items such that T c /• Each item is a binary variable representing whether an item was bought.
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Page 704 - Lin, HS Sawhney, and K. Shim. Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases.
Page 328 - Figure 1, includes an input layer, one or more hidden layers, and an output layer. The nodes in each layer are connected to each node in the adjacent layer.
Page 414 - Thus the time complexity of the algorithm is O(n) where n is the number of posts in a given configuration.
Page iv - Interfaces, & the Incremental Approach Michael L. Brodie and Michael Stonebraker Atomic Transactions Nancy Lynch, Michael Merritt, William Weihl, and Alan Fekete Query Processing for Advanced Database Systems Edited by Johann Christoph Freytag, David Maier, and Gottfried Vossen Transaction Processing: Concepts and Techniques Jim Gray and Andreas Reuter Understanding the New SQL: A Complete Guide Jim Melton and Alan R.
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Page 735 - G. Sheikholeslami, S. Chatterjee, and A. Zhang. Wavecluster: A multi-resolution clustering approach for very large spatial databases.
Page 706 - B. Babcock, S. Babu, M. Datar, R. Motwani, and J. Widom, Models and issues in data stream systems, in Proceedings of the 2002 ACM Symposium on Principles of Database Systems, June 2002, pp.
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References to this book
Bibliographic information
| Title | Data Mining, Southeast Asia Edition The Morgan Kaufmann Series in Data Management Systems |
| Authors | Jiawei Han, Jian Pei, Micheline Kamber |
| Edition | 2 |
| Publisher | Elsevier, 2006 |
| ISBN | 0080475582, 9780080475585 |
| Length | 800 pages |
| Export Citation | BiBTeX EndNote RefMan |