Data Algorithms

Recipes for Scaling Up with Hadoop and Spark
Author: Mahmoud Parsian
Publisher: "O'Reilly Media, Inc."
ISBN: 1491906154
Category: Computers
Page: 778
View: 8988
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If you are ready to dive into the MapReduce framework for processing large datasets, this practical book takes you step by step through the algorithms and tools you need to build distributed MapReduce applications with Apache Hadoop or Apache Spark. Each chapter provides a recipe for solving a massive computational problem, such as building a recommendation system. You’ll learn how to implement the appropriate MapReduce solution with code that you can use in your projects. Dr. Mahmoud Parsian covers basic design patterns, optimization techniques, and data mining and machine learning solutions for problems in bioinformatics, genomics, statistics, and social network analysis. This book also includes an overview of MapReduce, Hadoop, and Spark. Topics include: Market basket analysis for a large set of transactions Data mining algorithms (K-means, KNN, and Naive Bayes) Using huge genomic data to sequence DNA and RNA Naive Bayes theorem and Markov chains for data and market prediction Recommendation algorithms and pairwise document similarity Linear regression, Cox regression, and Pearson correlation Allelic frequency and mining DNA Social network analysis (recommendation systems, counting triangles, sentiment analysis)

Recent Advances in Data Mining of Enterprise Data: Algorithms and Applications


Author: N.A
Publisher: N.A
ISBN: 9814472174
Category:
Page: N.A
View: 4861
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Leadership Strategies in the Age of Big Data, Algorithms, and Analytics


Author: Norton Paley
Publisher: CRC Press
ISBN: 1498764150
Category: Business & Economics
Page: 286
View: 8602
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Harnessing the power of technology is one of the key measures of effective leadership. Leadership Strategies in the Age of Big Data, Algorithms, and Analytics will help leaders think and act like strategists to maintain a leading-edge competitive advantage. Written by a leading expert in the field, this book provides new insights on how to successfully transition companies by aligning an organization’s culture to accept the benefits of digital technology. The author emphasizes the importance of creating a team spirit with employees to embrace the digital age and develop strategic business plans that pinpoint new markets for growth, strengthen customer relationships, and develop competitive strategies. Understanding how to deal with inconsistencies when facts generated by data analytics disagree with your own experience, intuition, and knowledge of the competitive situation is key to successful leadership.

Data Structures And Algorithms


Author: A.A.Puntambekar
Publisher: Technical Publications
ISBN: 9788184316711
Category:
Page: 924
View: 3061
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Medical Computer Vision: Algorithms for Big Data

International Workshop, MCV 2014, Held in Conjunction with MICCAI 2014, Cambridge, MA, USA, September 18, 2014, Revised Selected Papers
Author: Bjoern Menze,Georg Langs,Albert Montillo,Michael Kelm,Henning Müller,Shaoting Zhang,Weidong (Tom) Cai,Dimitris Metaxas
Publisher: Springer
ISBN: 331913972X
Category: Computers
Page: 211
View: 9203
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This book constitutes the thoroughly refereed post-workshop proceedings of the International Workshop on Medical Computer Vision: Algorithms for Big Data, MCV 2014, held in Cambridge, MA, USA, in September 2019, in conjunction with the 17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014. The one-day workshop aimed at exploring the use of modern computer vision technology and "big data" algorithms in tasks such as automatic segmentation and registration, localization of anatomical features and detection of anomalies emphasizing questions of harvesting, organizing and learning from large-scale medical imaging data sets and general-purpose automatic understanding of medical images. The 18 full and 1 short papers presented in this volume were carefully reviewed and selected from 30 submission.

Kernel Based Algorithms for Mining Huge Data Sets

Supervised, Semi-supervised, and Unsupervised Learning
Author: Te-Ming Huang,Vojislav Kecman,Ivica Kopriva
Publisher: Springer Science & Business Media
ISBN: 3540316817
Category: Computers
Page: 260
View: 6379
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This is the first book treating the fields of supervised, semi-supervised and unsupervised machine learning collectively. The book presents both the theory and the algorithms for mining huge data sets using support vector machines (SVMs) in an iterative way. It demonstrates how kernel based SVMs can be used for dimensionality reduction and shows the similarities and differences between the two most popular unsupervised techniques.

We Are Data

Algorithms and the Making of Our Digital Selves
Author: John Cheney-Lippold
Publisher: NYU Press
ISBN: 1479808709
Category: Business & Economics
Page: 320
View: 8640
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What identity means in an algorithmic age: how it works, how our lives are controlled by it, and how we can resist it Algorithms are everywhere, organizing the near limitless data that exists in our world. Derived from our every search, like, click, and purchase, algorithms determine the news we get, the ads we see, the information accessible to us and even who our friends are. These complex configurations not only form knowledge and social relationships in the digital and physical world, but also determine who we are and who we can be, both on and offline. Algorithms create and recreate us, using our data to assign and reassign our gender, race, sexuality, and citizenship status. They can recognize us as celebrities or mark us as terrorists. In this era of ubiquitous surveillance, contemporary data collection entails more than gathering information about us. Entities like Google, Facebook, and the NSA also decide what that information means, constructing our worlds and the identities we inhabit in the process. We have little control over who we algorithmically are. Our identities are made useful not for us—but for someone else. Through a series of entertaining and engaging examples, John Cheney-Lippold draws on the social constructions of identity to advance a new understanding of our algorithmic identities. We Are Data will educate and inspire readers who want to wrest back some freedom in our increasingly surveilled and algorithmically-constructed world.

Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration


Author: Earl Cox
Publisher: Academic Press
ISBN: 0121942759
Category: Computers
Page: 530
View: 3034
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Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration is a handbook for analysts, engineers, and managers involved in developing data mining models in business and government. As you'll discover, fuzzy systems are extraordinarily valuable tools for representing and manipulating all kinds of data, and genetic algorithms and evolutionary programming techniques drawn from biology provide the most effective means for designing and tuning these systems. You don't need a background in fuzzy modeling or genetic algorithms to benefit, for this book provides it, along with detailed instruction in methods that you can immediately put to work in your own projects. The author provides many diverse examples and also an extended example in which evolutionary strategies are used to create a complex scheduling system. Written to provide analysts, engineers, and managers with the background and specific instruction needed to develop and implement more effective data mining systems Helps you to understand the trade-offs implicit in various models and model architectures Provides extensive coverage of fuzzy SQL querying, fuzzy clustering, and fuzzy rule induction Lays out a roadmap for exploring data, selecting model system measures, organizing adaptive feedback loops, selecting a model configuration, implementing a working model, and validating the final model In an extended example, applies evolutionary programming techniques to solve a complicated scheduling problem Presents examples in C, C++, Java, and easy-to-understand pseudo-code Extensive online component, including sample code and a complete data mining workbench

Data Mining and Knowledge Discovery with Evolutionary Algorithms


Author: Alex A. Freitas
Publisher: Springer Science & Business Media
ISBN: 3662049236
Category: Computers
Page: 265
View: 2664
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This book integrates two areas of computer science, namely data mining and evolutionary algorithms. Both these areas have become increasingly popular in the last few years, and their integration is currently an active research area. In general, data mining consists of extracting knowledge from data. The motivation for applying evolutionary algorithms to data mining is that evolutionary algorithms are robust search methods which perform a global search in the space of candidate solutions. This book emphasizes the importance of discovering comprehensible, interesting knowledge, which is potentially useful for intelligent decision making. The text explains both basic concepts and advanced topics

The Top Ten Algorithms in Data Mining


Author: Xindong Wu,Vipin Kumar
Publisher: CRC Press
ISBN: 9781420089653
Category: Computers
Page: 208
View: 7180
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Identifying some of the most influential algorithms that are widely used in the data mining community, The Top Ten Algorithms in Data Mining provides a description of each algorithm, discusses its impact, and reviews current and future research. Thoroughly evaluated by independent reviewers, each chapter focuses on a particular algorithm and is written by either the original authors of the algorithm or world-class researchers who have extensively studied the respective algorithm. The book concentrates on the following important algorithms: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. Examples illustrate how each algorithm works and highlight its overall performance in a real-world application. The text covers key topics—including classification, clustering, statistical learning, association analysis, and link mining—in data mining research and development as well as in data mining, machine learning, and artificial intelligence courses. By naming the leading algorithms in this field, this book encourages the use of data mining techniques in a broader realm of real-world applications. It should inspire more data mining researchers to further explore the impact and novel research issues of these algorithms.

A Practical Approach To Data Structures And Algorithms


Author: Sanjay Pahuja
Publisher: New Age International
ISBN: 8122420672
Category:
Page: 568
View: 9292
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Algorithms and Data Structures

The Basic Toolbox
Author: Kurt Mehlhorn,Peter Sanders
Publisher: Springer Science & Business Media
ISBN: 3540779779
Category: Computers
Page: 300
View: 5375
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This concise introduction is ideal for readers familiar with programming and basic mathematical language. It uses pictures, words and high-level pseudocode to explain algorithms and presents efficient implementations using real programming languages.

Data Structures and Algorithms


Author: Shi Kuo Chang
Publisher: World Scientific
ISBN: 9789812791245
Category: Computers
Page: 360
View: 8620
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This is an excellent, up-to-date and easy-to-use text on data structures and algorithms that is intended for undergraduates in computer science and information science. The thirteen chapters, written by an international group of experienced teachers, cover the fundamental concepts of algorithms and most of the important data structures as well as the concept of interface design. The book contains many examples and diagrams. Whenever appropriate, program codes are included to facilitate learning.This book is supported by an international group of authors who are experts on data structures and algorithms, through its website at http: //www.cs.pitt.edu/jung/GrowingBook/, so that both teachers and students can benefit from their expertise

Data Structures and Algorithms Using Java


Author: William McAllister
Publisher: Jones & Bartlett Publishers
ISBN: 1449612865
Category: Computers
Page: 580
View: 7681
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With an accessible writing style and manageable amount of content, Data Structures and Algorithms Using Java is the ideal text for your course. This outstanding text correlates to the recommended syllabus put forth by the Association of Computing Machinery standard curriculum guidelines. The author has produced a resource that is more readable and instructional than any other, without compromising the scope of the ACM CS103, Data Structures and Algorithms, course material. The text s unique, student-friendly pedagogical approach and organizational structure will keep students engaged in the process of self-directed investigative discovery both inside and outside the classroom. The pedagogical features of the text, based on the author s 30 years of teaching experience, include succinct code examples, a unique common template used as the organizational basis of each chapter, the use of pseudocode to present the major algorithms developed in the text, nearly 300 carefully designed figures, and a concise review of Java."

Genetic Algorithms + Data Structures = Evolution Programs


Author: Z. Michalewicz,Zbigniew Michalewicz
Publisher: Springer Science & Business Media
ISBN: 9783540606765
Category: Business & Economics
Page: 387
View: 9293
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The importance of these techniques is still growing, since evolution programs are parallel in nature, and parallelism is one of the most promising directions in computer science.

Data Structures and Efficient Algorithms

Final Report on the DFG Special Joint Initiative
Author: Burkhard Monien
Publisher: Springer Science & Business Media
ISBN: 9783540554882
Category: Computers
Page: 389
View: 6356
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Myocarditis and idiopathic dilated cardiomyopathy are being increasingly recognized as important causes of heart disease and heart failure. Immunological mechanisms have long been suspected as playing a role in thesediseases but direct evidence has been lacking. Recently, animal models have be- come available, in which myocarditis can be induced either by infection with cardiotropic viruses or by autoimmuniza- tion with heart-specific antigens. This book presents and analyzes the latest information obtained from experimental models, relating it to the practical problems of diagnosis and treatment of myocarditis.

Algorithms and Data Structures

Third Workshop, WADS '93, Montreal, Canada, August 11-13, 1993. Proceedings
Author: Frank Dehne,Jörg-Rüdiger Sack,Nicola Santoro,Sue Whitesides
Publisher: Springer Science & Business Media
ISBN: 9783540571551
Category: Computers
Page: 633
View: 6292
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The papers in this volume were presented at the Third Workshop on Algorithmsand Data Structures (WADS '93), held in Montreal, Canada, August 1993. The volume opens with five invited presentations: "Computing the all-pairs longest chains in the plane" by M.J. Atallah and D.Z. Chen, "Towards a better understanding of pure packet routing" by A. Borodin, "Tolerating faults in meshes and other networks" (abstract) by R. Cole, "A generalization of binary search" by R.M. Karp, and "Groups and algebraic complexity" (abstract) by A.C. Yao. The volume continues with 52 regular presentations selected from 165 submissions, each of which was evaluated by at least three program committee members, many of whom called upon additional reviewers.

An Introduction to Data Structures and Algorithms


Author: J.A. Storer
Publisher: Springer Science & Business Media
ISBN: 9780817642532
Category: Computers
Page: 599
View: 1109
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Data structures and algorithms are presented at the college level in a highly accessible format that presents material with one-page displays in a way that will appeal to both teachers and students. The thirteen chapters cover: Models of Computation, Lists, Induction and Recursion, Trees, Algorithm Design, Hashing, Heaps, Balanced Trees, Sets Over a Small Universe, Graphs, Strings, Discrete Fourier Transform, Parallel Computation. Key features: Complicated concepts are expressed clearly in a single page with minimal notation and without the "clutter" of the syntax of a particular programming language; algorithms are presented with self-explanatory "pseudo-code." * Chapters 1-4 focus on elementary concepts, the exposition unfolding at a slower pace. Sample exercises with solutions are provided. Sections that may be skipped for an introductory course are starred. Requires only some basic mathematics background and some computer programming experience. * Chapters 5-13 progress at a faster pace. The material is suitable for undergraduates or first-year graduates who need only review Chapters 1 -4. * This book may be used for a one-semester introductory course (based on Chapters 1-4 and portions of the chapters on algorithm design, hashing, and graph algorithms) and for a one-semester advanced course that starts at Chapter 5. A year-long course may be based on the entire book. * Sorting, often perceived as rather technical, is not treated as a separate chapter, but is used in many examples (including bubble sort, merge sort, tree sort, heap sort, quick sort, and several parallel algorithms). Also, lower bounds on sorting by comparisons are included with the presentation of heaps in the context of lower bounds for comparison-based structures. * Chapter 13 on parallel models of computation is something of a mini-book itself, and a good way to end a course. Although it is not clear what parallel

Data Structures and Algorithms in C++


Author: N.A
Publisher: Tata McGraw-Hill Education
ISBN: 9780070669109
Category: C++ (Computer program language)
Page: 360
View: 2295
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Implicit Curves and Surfaces: Mathematics, Data Structures and Algorithms


Author: Abel Gomes,Irina Voiculescu,Joaquim Jorge,Brian Wyvill,Callum Galbraith
Publisher: Springer Science & Business Media
ISBN: 9781848824065
Category: Computers
Page: 351
View: 4659
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Implicit objects have gained increasing importance in geometric modeling, visualisation, animation, and computer graphics, because their geometric properties provide a good alternative to traditional parametric objects. This book presents the mathematics, computational methods and data structures, as well as the algorithms needed to render implicit curves and surfaces, and shows how implicit objects can easily describe smooth, intricate, and articulatable shapes, and hence why they are being increasingly used in graphical applications. Divided into two parts, the first introduces the mathematics of implicit curves and surfaces, as well as the data structures suited to store their sampled or discrete approximations, and the second deals with different computational methods for sampling implicit curves and surfaces, with particular reference to how these are applied to functions in 2D and 3D spaces.