menu
{ "item_title" : "Text Mining", "item_author" : [" Sholom M. Weiss", "Nitin Indurkhya", "Tong Zhang "], "item_description" : "Text mining searches for regularities, patterns or trends in natural language text. Inspired by data mining, which discovers major patterns from highly structured databases, text mining aims to extract useful knowledge from unstructured text. This book focuses on the concepts and methods needed to expand horizons beyond structured, numeric data to automated mining of text samples. This authoritative and highly accessible text/reference, written by a team of authorities on text mining, develops the foundation concepts, principles, and methods needed to expand beyond structured, numeric data to automated mining of text samples. Researchers, computer scientists, and advanced undergraduates and graduates with work and interests in data mining, machine learning, databases, and computational linguistics will find the work an essential resource.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/1/44/192/996/1441929967_b.jpg", "price_data" : { "retail_price" : "169.99", "online_price" : "169.99", "our_price" : "169.99", "club_price" : "169.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Text Mining|Sholom M. Weiss

Text Mining : Predictive Methods for Analyzing Unstructured Information

local_shippingShip to Me
In Stock.
FREE Shipping for Club Members help

Overview

Text mining searches for regularities, patterns or trends in natural language text. Inspired by data mining, which discovers major patterns from highly structured databases, text mining aims to extract useful knowledge from unstructured text. This book focuses on the concepts and methods needed to expand horizons beyond structured, numeric data to automated mining of text samples. This authoritative and highly accessible text/reference, written by a team of authorities on text mining, develops the foundation concepts, principles, and methods needed to expand beyond structured, numeric data to automated mining of text samples. Researchers, computer scientists, and advanced undergraduates and graduates with work and interests in data mining, machine learning, databases, and computational linguistics will find the work an essential resource.

This item is Non-Returnable

Details

  • ISBN-13: 9781441929969
  • ISBN-10: 1441929967
  • Publisher: Springer
  • Publish Date: November 2010
  • Dimensions: 9.21 x 6.14 x 0.53 inches
  • Shipping Weight: 0.79 pounds
  • Page Count: 237

Related Categories

You May Also Like...

    1

BAM Customer Reviews