Mining of Massive Datasets, Leskovec Jure

Автор: T. Ravindra Babu; M. Narasimha Murty; S.V. Subrahm
Название: Compression Schemes for Mining Large Datasets
ISBN: 1447170555 ISBN-13(EAN): 9781447170556
Издательство: Springer
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Цена: 13275.00 р.
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Описание: This book addresses the challenges of data abstraction generation using the least number of database scans, compressing data through novel lossy and non-lossy schemes, and carrying out clustering and classification directly in the compressed domain.

Автор: Antony Unwin; Martin Theus; Heike Hofmann
Название: Graphics of Large Datasets
ISBN: 149393869X ISBN-13(EAN): 9781493938698
Издательство: Springer
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Цена: 19564.00 р.
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Описание: This book shows how to look at ways of visualizing large datasets, whether large in numbers of cases, or large in numbers of variables, or large in both. All ideas are illustrated with displays from analyses of real datasets.

Автор: Chao Zhang, Jiawei Han
Название: Multidimensional Mining of Massive Text Data
ISBN: 1681735210 ISBN-13(EAN): 9781681735214
Издательство: Mare Nostrum (Eurospan)
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Цена: 14692.00 р.
Наличие на складе: Нет в наличии.

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.

Автор: T. Ravindra Babu; M. Narasimha Murty; S.V. Subrahm
Название: Compression Schemes for Mining Large Datasets
ISBN: 1447156064 ISBN-13(EAN): 9781447156062
Издательство: Springer
Рейтинг:
Цена: 6986.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: This book addresses the challenges of data abstraction generation using the least number of database scans, compressing data through novel lossy and non-lossy schemes, and carrying out clustering and classification directly in the compressed domain.