Chao Zhang, Jiawei Han Multidimensional Mining of Massive Text Data 9781681735214

Варианты приобретения

Цена: 14692.00р.
Кол-во:
о цене
Наличие: Отсутствует.
Возможна поставка под заказ. Дата поступления на склад уточняется после оформления заказа

Добавить в корзину

в Мои желания

Автор: Chao Zhang, Jiawei Han
Название: Multidimensional Mining of Massive Text Data
ISBN: 9781681735214
Издательство: Mare Nostrum (Eurospan)
Классификация:

Этические и социальные аспекты ИТ

Цифровой стиль жизни

Социальные сети

Извлечение знаний из данных

Информационный поиск

ISBN-10: 1681735210
Обложка/Формат: Hardcover
Страницы: 198
Вес: 0.56 кг.
Дата издания: 30.03.2019
Серия: Synthesis lectures on data mining and knowledge discovery
Язык: English
Издание: New ed
Размер: 235 x 191 x 13
Ключевые слова: Data mining,Information retrieval,Social networking,Digital lifestyle, COMPUTERS / System Administration / Storage & Retrieval,COMPUTERS / Databases / Data Mining,COMPUTERS / Social Aspects / General
Рейтинг:
Поставляется из: Англии
Описание: Unstructured text, as one of the most important data forms, plays a crucial role in data-driven decision making in domains ranging from social networking and information retrieval to scientific research and healthcare informatics. In many emerging applications, people's information need from text data is becoming multidimensional—they demand useful insights along multiple aspects from a text corpus. However, acquiring such multidimensional knowledge from massive text data remains a challenging task. This book presents data mining techniques that turn unstructured text data into multidimensional knowledge. We investigate two core questions. (1) How does one identify task-relevant text data with declarative queries in multiple dimensions? (2) How does one distill knowledge from text data in a multidimensional space? To address the above questions, we develop a text cube framework. First, we develop a cube construction module that organizes unstructured data into a cube structure, by discovering latent multidimensional and multi-granular structure from the unstructured text corpus and allocating documents into the structure. Second, we develop a cube exploitation module that models multiple dimensions in the cube space, thereby distilling from user-selected data multidimensional knowledge. Together, these two modules constitute an integrated pipeline: leveraging the cube structure, users can perform multidimensional, multigranular data selection with declarative queries; and with cube exploitation algorithms, users can extract multidimensional patterns from the selected data for decision making. The proposed framework has two distinctive advantages when turning text data into multidimensional knowledge: flexibility and label-efficiency. First, it enables acquiring multidimensional knowledge flexibly, as the cube structure allows users to easily identify task-relevant data along multiple dimensions at varied granularities and further distill multidimensional knowledge. Second, the algorithms for cube construction and exploitation require little supervision; this makes the framework appealing for many applications where labeled data are expensive to obtain.

Multidimensional Mining of Massive Text Data, Chao Zhang, Jiawei Han