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Data has increased due to the growing use of web applications and
communication devices. It is necessary to develop new techniques of
managing data in order to ensure adequate usage. Modern
Technologies for Big Data Classification and Clustering is an
essential reference source for the latest scholarly research on
handling large data sets with conventional data mining and provide
information about the new technologies developed for the management
of large data. Featuring coverage on a broad range of topics such
as text and web data analytics, risk analysis, and opinion mining,
this publication is ideally designed for professionals,
researchers, and students seeking current research on various
concepts of big data analytics. Topics Covered: The many academic
areas covered in this publication include, but are not limited to:
Data visualization Distributed Computing Systems Opinion Mining
Privacy and security Risk analysis Social Network Analysis Text
Data Analytics Web Data Analytics
A Companion to the Archaeology of Religion in the Ancient World
presents a comprehensive overview of a wide range of topics
relating to the practices, expressions, and interactions of
religion in antiquity, primarily in the Greco-Roman world. -
Features readings that focus on religious experience and expression
in the ancient world rather than solely on religious belief -
Places a strong emphasis on domestic and individual religious
practice - Represents the first time that the concept of "lived
religion" is applied to the ancient history of religion and
archaeology of religion - Includes cutting-edge data taken from top
contemporary researchers and theorists in the field - Examines a
large variety of themes and religious traditions across a wide
geographical area and chronological span - Written to appeal
equally to archaeologists and historians of religion
DATA MINING AND MACHINE LEARNING APPLICATIONS The book elaborates
in detail on the current needs of data mining and machine learning
and promotes mutual understanding among research in different
disciplines, thus facilitating research development and
collaboration. Data, the latest currency of today's world, is the
new gold. In this new form of gold, the most beautiful jewels are
data analytics and machine learning. Data mining and machine
learning are considered interdisciplinary fields. Data mining is a
subset of data analytics and machine learning involves the use of
algorithms that automatically improve through experience based on
data. Massive datasets can be classified and clustered to obtain
accurate results. The most common technologies used include
classification and clustering methods. Accuracy and error rates are
calculated for regression and classification and clustering to find
actual results through algorithms like support vector machines and
neural networks with forward and backward propagation. Applications
include fraud detection, image processing, medical diagnosis,
weather prediction, e-commerce and so forth. The book features: A
review of the state-of-the-art in data mining and machine learning,
A review and description of the learning methods in human-computer
interaction, Implementation strategies and future research
directions used to meet the design and application requirements of
several modern and real-time applications for a long time, The
scope and implementation of a majority of data mining and machine
learning strategies. A discussion of real-time problems. Audience
Industry and academic researchers, scientists, and engineers in
information technology, data science and machine and deep learning,
as well as artificial intelligence more broadly.
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