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دانلود کتاب Data Clustering: Algorithms and Applications
دانلود کتاب خوشه بندی داده ها: الگوریتم ها و کاربردها
مشخصات کتاب
Data Clustering: Algorithms and Applications
ویرایش: [First edition] نویسندگان: Aggarwal. Charu C.(ed.), Reddy. Chandan K (ed.) سری: Chapman & Hall/CRC data mining and knowledge discovery series ISBN (شابک) : 9781315360416, 1315373513 ناشر: Chapman and Hall/CRC سال نشر: 2014 تعداد صفحات: 652 [648] زبان: English فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود) حجم فایل: 13 Mb
قیمت کتاب (تومان) : 80,000
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توضیحاتی در مورد کتاب خوشه بندی داده ها: الگوریتم ها و کاربردها
توضیحاتی درمورد کتاب به خارجی
Research on the problem of clustering tends to be fragmented
across the pattern recognition, database, data mining, and
machine learning communities. Addressing this problem in a
unified way, Data Clustering: Algorithms and Applications
provides complete coverage of the entire area of clustering,
from basic methods to more refined and complex data clustering
approaches. It pays special attention to recent issues in
graphs, social
networks, and other domains. The book focuses on three primary
aspects of data clustering: Methods, describing key techniques
commonly used for clustering, such as feature selection,
agglomerative clustering, partitional clustering, density-based
clustering, probabilistic clustering, grid-based clustering,
spectral clustering, and nonnegative matrix factorization.
Domains, covering methods used for different domains of data,
such as categorical data, text data, multimedia data, graph
data, biological data, stream data, uncertain data, time series
clustering, high-dimensional clustering, and big data
Variations and Insights, discussing important variations of the
clustering process, such as semisupervised clustering,
interactive clustering, multiview clustering, cluster
ensembles, and cluster validation. In this book, top
researchers from around the world explore the characteristics
of clustering problems in a variety of application areas. They
also explain how to glean detailed insight from the clustering
process-including how to verify the quality of the underlying
clusters-through supervision, human intervention, or the
automated generation of alternative clusters.
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Abstract: Research on the problem of clustering tends to be
fragmented across the pattern recognition, database, data
mining, and machine learning communities. Addressing this
problem in a unified way, Data Clustering: Algorithms and
Applications provides complete coverage of the entire area of
clustering, from basic methods to more refined and complex data
clustering approaches. It pays special attention to recent
issues in graphs, social networks, and other domains. The book
focuses on three primary aspects of data clustering: Methods,
describing key techniques commonly used for clustering, such as
feature selection, agglomerative clustering, partitional
clustering, density-based clustering, probabilistic clustering,
grid-based clustering, spectral clustering, and nonnegative
matrix factorization. Domains, covering methods used for
different domains of data, such as categorical data, text data,
multimedia data, graph data, biological data, stream data,
uncertain data, time series clustering, high-dimensional
clustering, and big data Variations and Insights, discussing
important variations of the clustering process, such as
semisupervised clustering, interactive clustering, multiview
clustering, cluster ensembles, and cluster validation. In this
book, top researchers from around the world explore the
characteristics of clustering problems in a variety of
application areas. They also explain how to glean detailed
insight from the clustering process-including how to verify the
quality of the underlying clusters-through supervision, human
intervention, or the automated generation of alternative
clusters
فهرست مطالب
Content: An Introduction to Cluster Analysis Charu C. Aggarwal --
Feature Selection for Clustering: A Review Salem Alelyani, Jiliang Tang, and Huan Liu --
Probabilistic Models for Clustering Hongbo Deng and Jiawei Han --
A Survey of Partitional and Hierarchical Clustering Algorithms Chandan K. Reddy and Bhanukiran Vinzamuri --
Density-Based Clustering Martin Ester --
Grid-Based Clustering Wei Cheng, Wei Wang, and Sandra Batista --
Non-Negative Matrix Factorizations for Clustering: A Survey Tao Li and Chris Ding --
Spectral Clustering Jialu Liu and Jiawei Han --
Clustering High-Dimensional Data Arthur Zimek --
A Survey of Stream Clustering Algorithms Charu C. Aggarwal --
Big Data Clustering Hanghang Tong and U. Kang --
Clustering Categorical Data Bill Andreopoulos --
Document Clustering: The Next Frontier David C. Anastasiu, Andrea Tagarelli, and George Karypis --
Clustering Multimedia Data Shen-Fu Tsai, Guo-Jun Qi, Shiyu Chang, Min-Hsuan Tsai, and Thomas S. Huang --
Time Series Data Clustering Dimitrios Kotsakos, Goce Trajcevski, Dimitrios Gunopulos, and Charu C. Aggarwal --
Clustering Biological Data Chandan K. Reddy, Mohammad Al Hasan, and Mohammed J. Zaki --
Network Clustering Srinivasan Parthasarathy and S.M. Faisal --
A Survey of Uncertain Data Clustering Algorithms Charu C. Aggarwal --
Concepts of Visual and Interactive Clustering Alexander Hinneburg --
Semi-Supervised Clustering Amrudin Agovic and Arindam Banerjee --
Alternative Clustering Analysis: A Review James Bailey --
Cluster Ensembles: Theory and Applications Joydeep Ghosh and Ayan Acharya --
Clustering Validation Measures Hui Xiong and Zhongmou Li --
Educational and Software Resources for Data Clustering Charu C. Aggarwal and Chandan K. Reddy --
Index.
