134,54 €
149,49 €
-10% with code: EXTRA
Multiple alternative clusterings and dimensionality reduction
Multiple alternative clusterings and dimensionality reduction
134,54
149,49 €
  • We will send in 10–14 business days.
Clustering is the process of grouping objects based on some notion of similarity. It is commonly applied for exploratory data analysis, segmentation, preprocessing and data summarization.Traditional clustering algorithms only find one clustering solution. However, data can be grouped and interpreted in many different ways. Moreover, different clustering solutions are interesting for different purposes. Instead of committing to one clustering solution, here we introduce four methods that can pro…
  • Publisher:
  • Year: 2014
  • Pages: 148
  • ISBN-10: 3639718860
  • ISBN-13: 9783639718867
  • Format: 15.2 x 22.9 x 0.9 cm, softcover
  • Language: English
  • SAVE -10% with code: EXTRA

Multiple alternative clusterings and dimensionality reduction (e-book) (used book) | bookbook.eu

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Clustering is the process of grouping objects based on some notion of similarity. It is commonly applied for exploratory data analysis, segmentation, preprocessing and data summarization.Traditional clustering algorithms only find one clustering solution. However, data can be grouped and interpreted in many different ways. Moreover, different clustering solutions are interesting for different purposes. Instead of committing to one clustering solution, here we introduce four methods that can provide several possible alternative clustering solutions to the user for exploratory data analysis.

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  • Author: Donglin Niu
  • Publisher:
  • Year: 2014
  • Pages: 148
  • ISBN-10: 3639718860
  • ISBN-13: 9783639718867
  • Format: 15.2 x 22.9 x 0.9 cm, softcover
  • Language: English English

Clustering is the process of grouping objects based on some notion of similarity. It is commonly applied for exploratory data analysis, segmentation, preprocessing and data summarization.Traditional clustering algorithms only find one clustering solution. However, data can be grouped and interpreted in many different ways. Moreover, different clustering solutions are interesting for different purposes. Instead of committing to one clustering solution, here we introduce four methods that can provide several possible alternative clustering solutions to the user for exploratory data analysis.

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