Data Mining, Southeast Asia Edition: Concepts and Techniques (Google eBook)
Our 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.
Whether you are a seasoned professional or a new student of data mining, this book has much to offer you:
* A comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data.
* Updates that incorporate input from readers, changes in the field, and more material on statistics and machine learning.
* Dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects.
* Complete classroom support for instructors at www.mkp.com/datamining2e companion site.
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8 Mining Stream TimeSeries and Sequence Data
9 Graph Mining Social Network Analysis and Multirelational Data Mining
10 Mining Object Spatial Multimedia Text and Web Data
11 Applications and Trends in Data Mining
An Introduction to Microsofts OLE DB for Data Mining
aggregate algorithm AllElectronics applications approach Apriori association rules attribute Bayesian categorical cells Chapter class label classification cluster analysis clustering methods concept hierarchy Conf constraints contains count crosstab cuboid data analysis data cube data marts data mining system data set data streams data warehouse data warehousing database systems decision tree defined described dimension table dimensions distribution documents efficient example Figure frequent itemsets frequent patterns function given graph graph mining iceberg cube induction input k-medoids Knowledge Discovery machine learning measure minimum support multidimensional multimedia data multiple multirelational neural network node objects OLAP on-line outliers partitioning pattern mining performed prediction Proc pruning regression relational database represented retrieval ROLAP scalable schema Section selection sequence similar space spatial data specified statistical stored stream data structure subset techniques threshold tion training tuples transaction transformation tuples typically values variables vector visualization
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