Text Mining: Predictive Methods for Analyzing Unstructured Information (Paperback)
暫譯: 文本挖掘:分析非結構化資訊的預測方法 (平裝本)

Sholom M. M. Weiss

  • 出版商: Springer
  • 出版日期: 2010-11-19
  • 售價: $6,930
  • 貴賓價: 9.5$6,584
  • 語言: 英文
  • 頁數: 252
  • 裝訂: Paperback
  • ISBN: 1441929967
  • ISBN-13: 9781441929969
  • 相關分類: Text-miningMachine Learning
  • 海外代購書籍(需單獨結帳)

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商品描述

The growth of the web can be seen as an expanding public digital library collection. Online digital information extends far beyond the web and its publicly available information. Huge amounts of information are private and are of interest to local communities, such as the records of customers of a business. This information is overwhelmingly text and has its record-keeping purpose, but an automated analysis might be desirable to find patterns in the stored records. Analogous to this data mining is text mining, which also finds patterns and trends in information samples but which does so with far less structured--though with greater immediate utility for users--ingredients. This book focuses on the concepts and methods needed to expand horizons beyond structured, numeric data to automated mining of text samples. It introduces the new world of text mining and examines proven methods for various critical text-mining tasks, such as automated document indexing and information retrieval and search. New research areas are explored, such as information extraction and document summarization, that rely on evolving text-mining techniques.

商品描述(中文翻譯)

網路的成長可以視為一個不斷擴展的公共數位圖書館收藏。線上數位資訊的範圍遠超過網路及其公開可用的資訊。大量資訊是私有的,並且對當地社區具有興趣,例如企業的客戶記錄。這些資訊主要是文本,具有記錄保存的目的,但自動化分析可能是必要的,以便在儲存的記錄中尋找模式。與此數據挖掘類似的是文本挖掘,它也在資訊樣本中尋找模式和趨勢,但其成分結構較少——雖然對用戶的即時效用更大。本書專注於擴展視野所需的概念和方法,超越結構化的數值數據,進行文本樣本的自動化挖掘。它介紹了文本挖掘的新世界,並檢視各種關鍵文本挖掘任務的成熟方法,例如自動文檔索引和資訊檢索與搜尋。還探討了依賴不斷演變的文本挖掘技術的新研究領域,如資訊擷取和文檔摘要。