 |
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| Boris Mirkin | ||||
| Chapman & Hall | ||||
| 2005 | ||||
| English | ||||
| 249 pages | ||||
| 3.88 MB | ||||
[tab] [content title="Summary"] Often regarded as more of an art than a science, clustering has traditionally relied on example-driven learning and trial-and-error techniques. Even well-known methods like K-Means for partitioning and Ward's method for hierarchical clustering have not received the theoretical scrutiny needed to establish clear connections between them and provide meaningful interpretation aids. **Clustering for Data Mining: A Data Recovery Approach** shifts away from these ad hoc techniques by presenting a theory that bridges gaps in both K-Means and Ward methods, extending their applicability to contemporary topics such as clustering mixed-scale data and handling incomplete clustering. The author introduces innovative methods for cluster detection and description, discusses related areas such as principal component analysis, contingency measures, and data visualization, and includes nearly 60 computational examples that cover all stages of clustering—from data preprocessing to cluster validation and result interpretation. With a focus on data recovery methods, theory-based guidance, and practical instructions for real-world data mining, this book is well-suited for a variety of purposes: teaching, self-study, and professional reference. [/content] [content title="Content"] [/content] [content title="Author(s)"] [/content] [/tab]
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