Placeholder text

Go to cart

Clustering for Data Mining: A Data Recovery Approach

By Boris Mirkin

Clustering for Data Mining: A Data Recovery Approach Computer Science

Clustering for Data Mining: A Data Recovery Approach

By Boris Mirkin

0 - Default Title
Description
Often considered more as an art than a science, the field of clustering has been dominated by learning through examples and by techniques chosen almost through trial-and-error. Even the most popular clustering methods--K-Means for partitioning the data set and Ward's method for hierarchical clustering--have lacked the theoretical attention that would establish a firm relationship between the two methods and relevant interpretation aids.Rather than the traditional set of ad hoc techniques, Clustering for Data Mining: A Data Recovery Approach presents a theory that not only closes gaps in K-Means and Ward methods, but also extends them into areas of current interest, such as clustering mixed scale data and incomplete clustering. The author suggests original methods for both cluster finding and cluster description, addresses related topics such as principal component analysis, contingency measures, and data visualization, and includes nearly 60 computational examples covering all stages of clustering, from data pre-processing to cluster validation and results interpretation.T his author's unique attention to data recovery methods, theory-based advice, pre- and post-processing issues t
Product details
Edition:
1
Number of Pages:
266
Release Date:
2005-04-25
Publication Date:
2005-04-30
Publisher:
CHAPMAN & HALL
Languages:
Original: English
ISBN10:
1584885343
ISBN13:
9781584885344
Weight:
567 g
Height:
164 cm
Width:
242 cm
Thickness:
22 cm
Buy on Amazon
Currently sold out