K-Means Multidimensional Big Data Clusters Through Cloud PDF Download
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Author: Agnivesh
Publisher: A.K. Publications
ISBN: 9789421015015
Category :
Languages : en
Pages : 0
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Book Description
Contemporary researchers find a scenario where almost entire humanity, barring a small percentage of it, is creating data, storing data and using data in a very large scale unstopped. The human race has become data dependent as never before. If infinite had certain defined limits big data would have been a synonym to infinite or almost tending to it in due course of time. Researchers are tackling with the analytics of this big data for making it most useful by evolving various methods. It is growingly desired to reduce infinitesimally the time being taken in the process of analytics. Cloud computing is a computing infrastructure model which implements complex processing in massive scale. It eliminates requirement of maintaining costlier computing hardware and large space requirement. Basic aim behind cloud computing model is to offer processing power, space for data and applications in form of service. Clustering is a powerful big data analytics and prediction technique . The process divides a dataset into groups. These groups are called clusters. Elements of each partition are as close as possible to one another, and elements of different groups are as far as possible from one another . It uncovers hidden information from a dataset. The information is vital for an organization to take right decisions. For example, clustering helps to find out different groups of customers by analyzing their purchasing patterns and choices in trade and business. Similarly, clustering helps in categorizing different species of plants and animals considering their various properties . There are many clustering methods to solve different types of problems. K-means is used widely for clustering . It finds homogenous objects on the basis of distance vectors suited to small datasets. Pre-specifying clusters count and a dataset are the two inputs to the process. By applying trial-and-error method, it finds number of clusters accurately for a given dataset. Moreover, initial centres are selected randomly. This is initialization step of the algorithm. Second step is classification which measures Euclidean distance between these centres and objects. An objects is allocated to its closest centre. Then, average of the points of each cluster is calculated. These averages or means are new centres of the clusters. Final step is convergence step. The process stops as soon as no points migrate from one cluster to another.
Author: Agnivesh
Publisher: A.K. Publications
ISBN: 9789421015015
Category :
Languages : en
Pages : 0
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Book Description
Contemporary researchers find a scenario where almost entire humanity, barring a small percentage of it, is creating data, storing data and using data in a very large scale unstopped. The human race has become data dependent as never before. If infinite had certain defined limits big data would have been a synonym to infinite or almost tending to it in due course of time. Researchers are tackling with the analytics of this big data for making it most useful by evolving various methods. It is growingly desired to reduce infinitesimally the time being taken in the process of analytics. Cloud computing is a computing infrastructure model which implements complex processing in massive scale. It eliminates requirement of maintaining costlier computing hardware and large space requirement. Basic aim behind cloud computing model is to offer processing power, space for data and applications in form of service. Clustering is a powerful big data analytics and prediction technique . The process divides a dataset into groups. These groups are called clusters. Elements of each partition are as close as possible to one another, and elements of different groups are as far as possible from one another . It uncovers hidden information from a dataset. The information is vital for an organization to take right decisions. For example, clustering helps to find out different groups of customers by analyzing their purchasing patterns and choices in trade and business. Similarly, clustering helps in categorizing different species of plants and animals considering their various properties . There are many clustering methods to solve different types of problems. K-means is used widely for clustering . It finds homogenous objects on the basis of distance vectors suited to small datasets. Pre-specifying clusters count and a dataset are the two inputs to the process. By applying trial-and-error method, it finds number of clusters accurately for a given dataset. Moreover, initial centres are selected randomly. This is initialization step of the algorithm. Second step is classification which measures Euclidean distance between these centres and objects. An objects is allocated to its closest centre. Then, average of the points of each cluster is calculated. These averages or means are new centres of the clusters. Final step is convergence step. The process stops as soon as no points migrate from one cluster to another.
Author: Weizhong Qiang
Publisher: Springer
ISBN: 3319284304
Category : Computers
Languages : en
Pages : 409
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Book Description
This book constitutes the refereed proceedings of the Second International Conference on Cloud Computing and Big Data, CloudCom-Asia 2015, held in Huangshan, China, in June 2015. The 29 full papers and two keynote speeches were carefully reviewed and selected from 106 submissions. The papers are organized in topical sections on cloud architecture; applications; big data and social network; security and privacy.
Author: Jake VanderPlas
Publisher: "O'Reilly Media, Inc."
ISBN: 1491912138
Category : Computers
Languages : en
Pages : 743
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Book Description
For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms
Author: Ma, Zongmin
Publisher: IGI Global
ISBN: 1466698357
Category : Computers
Languages : en
Pages : 333
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Book Description
Cloud computing has proven to be a successful paradigm of service-oriented computing, and has revolutionized the way computing infrastructures are abstracted and used. By means of cloud computing technology, massive data can be managed effectively and efficiently to support various aspects of problem solving and decision making. Managing Big Data in Cloud Computing Environments explores the latest advancements in the area of data management and analysis in the cloud. Providing timely, research-based information relating to data storage, sharing, extraction, and indexing in cloud systems, this publication is an ideal reference source for graduate students, IT specialists, researchers, and professionals working in the areas of data and knowledge engineering.
Author: Elijah Blessing Rajsingh
Publisher: Springer
ISBN: 9811072000
Category : Technology & Engineering
Languages : en
Pages : 402
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Book Description
This book is a compendium of the proceedings of the International Conference on Big-Data and Cloud Computing. It includes recent advances in the areas of big data analytics, cloud computing, the Internet of nano things, cloud security, data analytics in the cloud, smart cities and grids, etc. Primarily focusing on the application of knowledge that promotes ideas for solving the problems of the society through cutting-edge technologies, it provides novel ideas that further world-class research and development. This concise compilation of articles approved by a panel of expert reviewers is an invaluable resource for researchers in the area of advanced engineering sciences.
Author: C. Catlett
Publisher: IOS Press
ISBN: 161499322X
Category : Computers
Languages : en
Pages : 260
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Book Description
Cloud computing offers many advantages to researchers and engineers who need access to high performance computing facilities for solving particular compute-intensive and/or large-scale problems, but whose overall high performance computing (HPC) needs do not justify the acquisition and operation of dedicated HPC facilities. There are, however, a number of fundamental problems which must be addressed, such as the limitations imposed by accessibility, security and communication speed, before these advantages can be exploited to the full. This book presents 14 contributions selected from the International Research Workshop on Advanced High Performance Computing Systems, held in Cetraro, Italy, in June 2012. The papers are arranged in three chapters. Chapter 1 includes five papers on cloud infrastructures, while Chapter 2 discusses cloud applications. The third chapter in the book deals with big data, which is nothing new – large scientific organizations have been collecting large amounts of data for decades – but what is new is that the focus has now broadened to include sectors such as business analytics, financial analyses, Internet service providers, oil and gas, medicine, automotive and a host of others. This book will be of interest to all those whose work involves them with aspects of cloud computing and big data applications.
Author: Junjie Wu
Publisher: Springer Science & Business Media
ISBN: 3642298079
Category : Computers
Languages : en
Pages : 187
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Book Description
Nearly everyone knows K-means algorithm in the fields of data mining and business intelligence. But the ever-emerging data with extremely complicated characteristics bring new challenges to this "old" algorithm. This book addresses these challenges and makes novel contributions in establishing theoretical frameworks for K-means distances and K-means based consensus clustering, identifying the "dangerous" uniform effect and zero-value dilemma of K-means, adapting right measures for cluster validity, and integrating K-means with SVMs for rare class analysis. This book not only enriches the clustering and optimization theories, but also provides good guidance for the practical use of K-means, especially for important tasks such as network intrusion detection and credit fraud prediction. The thesis on which this book is based has won the "2010 National Excellent Doctoral Dissertation Award", the highest honor for not more than 100 PhD theses per year in China.
Author: Borko Furht
Publisher: Springer
ISBN: 3319445502
Category : Computers
Languages : en
Pages : 405
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Book Description
The objective of this book is to introduce the basic concepts of big data computing and then to describe the total solution of big data problems using HPCC, an open-source computing platform. The book comprises 15 chapters broken into three parts. The first part, Big Data Technologies, includes introductions to big data concepts and techniques; big data analytics; and visualization and learning techniques. The second part, LexisNexis Risk Solution to Big Data, focuses on specific technologies and techniques developed at LexisNexis to solve critical problems that use big data analytics. It covers the open source High Performance Computing Cluster (HPCC Systems®) platform and its architecture, as well as parallel data languages ECL and KEL, developed to effectively solve big data problems. The third part, Big Data Applications, describes various data intensive applications solved on HPCC Systems. It includes applications such as cyber security, social network analytics including fraud, Ebola spread modeling using big data analytics, unsupervised learning, and image classification. The book is intended for a wide variety of people including researchers, scientists, programmers, engineers, designers, developers, educators, and students. This book can also be beneficial for business managers, entrepreneurs, and investors.
Author: Kai Hwang
Publisher: MIT Press
ISBN: 026203641X
Category : Computers
Languages : en
Pages : 626
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Book Description
The first textbook to teach students how to build data analytic solutions on large data sets using cloud-based technologies. This is the first textbook to teach students how to build data analytic solutions on large data sets (specifically in Internet of Things applications) using cloud-based technologies for data storage, transmission and mashup, and AI techniques to analyze this data. This textbook is designed to train college students to master modern cloud computing systems in operating principles, architecture design, machine learning algorithms, programming models and software tools for big data mining, analytics, and cognitive applications. The book will be suitable for use in one-semester computer science or electrical engineering courses on cloud computing, machine learning, cloud programming, cognitive computing, or big data science. The book will also be very useful as a reference for professionals who want to work in cloud computing and data science. Cloud and Cognitive Computing begins with two introductory chapters on fundamentals of cloud computing, data science, and adaptive computing that lay the foundation for the rest of the book. Subsequent chapters cover topics including cloud architecture, mashup services, virtual machines, Docker containers, mobile clouds, IoT and AI, inter-cloud mashups, and cloud performance and benchmarks, with a focus on Google's Brain Project, DeepMind, and X-Lab programs, IBKai HwangM SyNapse, Bluemix programs, cognitive initiatives, and neurocomputers. The book then covers machine learning algorithms and cloud programming software tools and application development, applying the tools in machine learning, social media, deep learning, and cognitive applications. All cloud systems are illustrated with big data and cognitive application examples.
Author: Haruna Chiroma
Publisher: Springer Nature
ISBN: 3030662888
Category : Technology & Engineering
Languages : en
Pages : 316
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Book Description
This book addresses theories and empirical procedures for the application of machine learning and data mining to solve problems in cyber dynamics. It explains the fundamentals of cyber dynamics, and presents how these resilient algorithms, strategies, techniques can be used for the development of the cyberspace environment such as: cloud computing services; cyber security; data analytics; and, disruptive technologies like blockchain. The book presents new machine learning and data mining approaches in solving problems in cyber dynamics. Basic concepts, related work reviews, illustrations, empirical results and tables are integrated in each chapter to enable the reader to fully understand the concepts, methodology, and the results presented. The book contains empirical solutions of problems in cyber dynamics ready for industrial applications. The book will be an excellent starting point for postgraduate students and researchers because each chapter is design to have future research directions.
