Data Mining in Crystallography

Name: Data Mining in Crystallography
Brand: Springer Nature
SKU: 9783642047589
Availability: InStock

Best Price (Coupon Required):
Buy Data Mining in Crystallography for $152.10 at @ Link.springer.com when you apply the 10% OFF coupon at checkout.
Click “Get Coupon & Buy” to copy the code and unlock the deal.

Set a price drop alert to never miss an offer.

1 Offer Price Range: $169.00 - $169.00

BEST PRICE

Single Product Purchase

$152.10

@ Link.springer.com with extra coupon

Price Comparison

Seller	Contact Seller	List Price	On Sale	Shipping	Best Promo	Final Price	Volume Discount	Financing	Availability	Seller's Page
BEST PRICE 1 Product Purchase @ Link.springer.com		$169.00	$169.00		10% OFF This deals requires coupon	$152.10		See Site	In stock	Visit Store

Product Details

Brand

Springer Nature

Manufacturer

N/A

Part Number

GTIN

9783642047589

Condition

New

Product Description

Humans have been manually extracting patterns from data for centuries, but the increasing volume of data in modern times has called for more automatic approaches. Early methods of identifying patterns in data include Bayes theorem (1700s) and Regression analysis (1800s). The proliferation, ubiquity and incre- ing power of computer technology has increased data collection and storage. As data sets have grown in size and complexity, direct hands-on data analysis has - creasingly been augmented with indirect, automatic data processing. Data mining has been developed as the tool for extracting hidden patterns from data, by using computing power and applying new techniques and methodologies for knowledge discovery. This has been aided by other discoveries in computer science, such as Neural networks, Clustering, Genetic algorithms (1950s), Decision trees (1960s) and Support vector machines (1980s). Data mining commonlyinvolves four classes of tasks: Classi cation: Arranges the data into prede ned groups. For example, an e-mail program might attempt to classify an e-mail as legitimate or spam. Common algorithmsinclude Nearest neighbor,Naive Bayes classi er and Neural network. Clustering: Is like classi cation but the groups are not prede ned, so the algorithm will try to group similar items together. Regression: Attempts to nd a function which models the data with the least error. A common method is to use Genetic Programming. Association rule learning: Searches for relationships between variables. For example, a supermarket might gather data of what each customer buys.

Available Colors

N/A

Available Sizes

N/A