Data Mining, Southeast Asia Edition
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.
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Snowflakes,. and. Fact. Constellations: Schemas. for. Multidimensional.
Databases. The entity-relationship data model is commonly used in ... model can
exist in the form of a star schema, a snowflake schema, or a fact constellation
The major difference between the snowflake and star schema models is that the
dimension tables of the snowflake model may be kept in normalized form to
reduce redundancies. Such a table is easy to maintain and saves storage space.
This kind of schema can be viewed as a collection of stars, and hence is called a
galaxy schema or a fact constellation. ... For data marts, the star or snowflake
schema are commonly used, since both are geared toward modeling single ...
Snowflake schema definition. The snowflake schema of Example 3.2 and Figure
3.5 is defined in DMQL as follows: define cube sales snowflake [time, item,
branch, location]: dollars sold = sum(salesindollars), units sold = count(*) define ...
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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