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Cluster analysis

Jiawei Han and Micheline Kamber
07.18.2001
Rating: -3.50- (out of 5)

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Cluster analysis
Jiawei Han and Micheline Kamber

With the huge amount of data available to most data warehouses, finding ways to organize that data is a top priority. According to Han and Kamber's book Data Mining: Concepts and Techniques (Morgan Kaufman), clustering is a viable option for organizing data that has not been classified yet.

The process of grouping a set of physical or abstract objects into classes of similar objects is called clustering. A cluster is a collection of data objects that are similar to one another with the same cluster and are dissimilar to the objects in other clusters. A cluster of data objects can be treated collectively as one group in many applications.

In data minin

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g, efforts have focused on finding methods for efficient and effective cluster analysis in large databases. Active themes of research focus on the scalability of clustering methods, the effectiveness of methods for clustering complex shapes and types of data, high-dimensional clustering techniques, and methods for clustering mixed numerical and categorical data in large databases.

Clustering is a challenging field of research where its potential applications pose their own special requirements. The following are typical requirements of clustering in data mining:

Click on the book title to learn more about Data Data Mining: Concepts and Techniques.

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