Foundations and Methods in Combinatorial and Statistical Data Analysis and Clustering, Lerman Israлl Cйsar
Автор: Isra?l C?sar Lerman Название: Foundations and Methods in Combinatorial and Statistical Data Analysis and Clustering ISBN: 1447167910 ISBN-13(EAN): 9781447167914 Издательство: Springer Рейтинг: Цена: 153720.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Preface.- On Some Facets of the Partition Set of a Finite Set.- Two Methods of Non-hierarchical Clustering.- Structure and Mathematical Representation of Data.- Ordinal and Metrical Analysis of the Resemblance Notion.- Comparing Attributes by a Probabilistic and Statistical Association I.- Comparing Attributes by a Probabilistic and Statistical Association II.- Comparing Objects or Categories Described by Attributes.- The Notion of "Natural" Class, Tools for its Interpretation. The Classifiability Concept.- Quality Measures in Clustering.- Building a Classification Tree.- Applying the LLA Method to Real Data.- Conclusion and Thoughts for Future Works
Автор: Olfa Nasraoui; Chiheb-Eddine Ben N`Cir Название: Clustering Methods for Big Data Analytics ISBN: 3319978632 ISBN-13(EAN): 9783319978635 Издательство: Springer Рейтинг: Цена: 139750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book highlights the state of the art and recent advances in Big Data clustering methods and their innovative applications in contemporary AI-driven systems. The book chapters discuss Deep Learning for Clustering, Blockchain data clustering, Cybersecurity applications such as insider threat detection, scalable distributed clustering methods for massive volumes of data; clustering Big Data Streams such as streams generated by the confluence of Internet of Things, digital and mobile health, human-robot interaction, and social networks; Spark-based Big Data clustering using Particle Swarm Optimization; and Tensor-based clustering for Web graphs, sensor streams, and social networks. The chapters in the book include a balanced coverage of big data clustering theory, methods, tools, frameworks, applications, representation, visualization, and clustering validation.
Автор: Olfa Nasraoui; Chiheb-Eddine Ben N`Cir Название: Clustering Methods for Big Data Analytics ISBN: 3030074196 ISBN-13(EAN): 9783030074197 Издательство: Springer Рейтинг: Цена: 139750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book highlights the state of the art and recent advances in Big Data clustering methods and their innovative applications in contemporary AI-driven systems. The book chapters discuss Deep Learning for Clustering, Blockchain data clustering, Cybersecurity applications such as insider threat detection, scalable distributed clustering methods for massive volumes of data; clustering Big Data Streams such as streams generated by the confluence of Internet of Things, digital and mobile health, human-robot interaction, and social networks; Spark-based Big Data clustering using Particle Swarm Optimization; and Tensor-based clustering for Web graphs, sensor streams, and social networks. The chapters in the book include a balanced coverage of big data clustering theory, methods, tools, frameworks, applications, representation, visualization, and clustering validation.
Автор: Seetha Hari, Murty M. Narasimha, Tripathy B. K. Название: Modern Technologies for Big Data Classification and Clustering ISBN: 1522528059 ISBN-13(EAN): 9781522528050 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 208830.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: 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 visualizationDistributed Computing SystemsOpinion MiningPrivacy and securityRisk analysisSocial Network AnalysisText Data AnalyticsWeb Data Analytics
Автор: Jin Cheqing, Zhou Aoying, Mao Jiali Название: Clustering And Outlier Detection For Trajectory Stream Data ISBN: 9811210454 ISBN-13(EAN): 9789811210457 Издательство: World Scientific Publishing Рейтинг: Цена: 95040.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
As mobile devices continue becoming a larger part of our lives, the development of location acquisition technologies to track moving objects have focused the minds of researchers on issues ranging from longitude and latitude coordinates, speed, direction, and timestamping, as part of parameters needed to calculate the positional information and locations of objects, in terms of time and position in the form of trajectory streams. Recently, recent advances have facilitated various urban applications such as smart transportation and mobile delivery services.
Unlike other books on spatial databases, mobile computing, data mining, or computing with spatial trajectories, this book is focused on smart transportation applications.
This book is a good reference for advanced undergraduates, graduate students, researchers, and system developers working on transportation systems.
Автор: V.S. Kumbhar, K.S. Oza, R.K. Kamat Название: Web Mining: A Synergic Approach Resorting to Classification and Clustering ISBN: 8793379838 ISBN-13(EAN): 9788793379831 Издательство: Taylor&Francis Рейтинг: Цена: 73490.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Showcases an effective methodology for classification and clustering of web sites from a usability point of view. While the clustering and classification is accomplished by using an open source tool, WEKA, the basic dataset for the selected websites has been arrived at by using a free tool site-analyser. As a case study, several commercial websites are analysed.
Автор: Francesco Masulli; Alfredo Petrosino; Stefano Rove Название: Clustering High--Dimensional Data ISBN: 3662485761 ISBN-13(EAN): 9783662485767 Издательство: Springer Рейтинг: Цена: 37270.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book constitutes the proceedings of the International Workshop on Clustering High-Dimensional Data, CHDD 2012, held in Naples, Italy, in May 2012. and the most common approach to tackle dimensionality problems, namely, dimensionality reduction and its application in clustering.
Автор: Lei Meng; Ah-Hwee Tan; Donald C. Wunsch II Название: Adaptive Resonance Theory in Social Media Data Clustering ISBN: 3030029840 ISBN-13(EAN): 9783030029845 Издательство: Springer Рейтинг: Цена: 93160.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Social media data contains our communication and online sharing, mirroring our daily life. This book looks at how we can use and what we can discover from such big data:Basic knowledge (data & challenges) on social media analyticsClustering as a fundamental technique for unsupervised knowledge discovery and data miningA class of neural inspired algorithms, based on adaptive resonance theory (ART), tackling challenges in big social media data clustering Step-by-step practices of developing unsupervised machine learning algorithms for real-world applications in social media domainAdaptive Resonance Theory in Social Media Data Clustering stands on the fundamental breakthrough in cognitive and neural theory, i.e. adaptive resonance theory, which simulates how a brain processes information to perform memory, learning, recognition, and prediction.It presents initiatives on the mathematical demonstration of ART’s learning mechanisms in clustering, and illustrates how to extend the base ART model to handle the complexity and characteristics of social media data and perform associative analytical tasks.Both cutting-edge research and real-world practices on machine learning and social media analytics are included in the book and if you wish to learn the answers to the following questions, this book is for you:How to process big streams of multimedia data?How to analyze social networks with heterogeneous data?How to understand a user’s interests by learning from online posts and behaviors?How to create a personalized search engine by automatically indexing and searching multimodal information resources? .
Автор: Long, Bo , Zhang, Zhongfei , Yu, Philip S. Название: Relational Data Clustering ISBN: 0367384051 ISBN-13(EAN): 9780367384050 Издательство: Taylor&Francis Рейтинг: Цена: 63280.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
A culmination of the authors' years of extensive research on this topic, Relational Data Clustering: Models, Algorithms, and Applications addresses the fundamentals and applications of relational data clustering. It describes theoretic models and algorithms and, through examples, shows how to apply these models and algorithms to solve real-world problems.
After defining the field, the book introduces different types of model formulations for relational data clustering, presents various algorithms for the corresponding models, and demonstrates applications of the models and algorithms through extensive experimental results. The authors cover six topics of relational data clustering:
Clustering on bi-type heterogeneous relational data
Multi-type heterogeneous relational data
Homogeneous relational data clustering
Clustering on the most general case of relational data
Individual relational clustering framework
Recent research on evolutionary clustering
This book focuses on both practical algorithm derivation and theoretical framework construction for relational data clustering. It provides a complete, self-contained introduction to advances in the field.
Автор: Gan, Guojun Название: Data Clustering in C++ ISBN: 0367382954 ISBN-13(EAN): 9780367382957 Издательство: Taylor&Francis Рейтинг: Цена: 65320.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Data clustering is a highly interdisciplinary field, the goal of which is to divide a set of objects into homogeneous groups such that objects in the same group are similar and objects in different groups are quite distinct. Thousands of theoretical papers and a number of books on data clustering have been published over the past 50 years. However, few books exist to teach people how to implement data clustering algorithms. This book was written for anyone who wants to implement or improve their data clustering algorithms.
Using object-oriented design and programming techniques, Data Clustering in C++ exploits the commonalities of all data clustering algorithms to create a flexible set of reusable classes that simplifies the implementation of any data clustering algorithm. Readers can follow the development of the base data clustering classes and several popular data clustering algorithms. Additional topics such as data pre-processing, data visualization, cluster visualization, and cluster interpretation are briefly covered.
This book is divided into three parts--
Data Clustering and C++ Preliminaries: A review of basic concepts of data clustering, the unified modeling language, object-oriented programming in C++, and design patterns
A C++ Data Clustering Framework: The development of data clustering base classes
Data Clustering Algorithms: The implementation of several popular data clustering algorithms
A key to learning a clustering algorithm is to implement and experiment the clustering algorithm. Complete listings of classes, examples, unit test cases, and GNU configuration files are included in the appendices of this book as well as in the downloadable resources. The only requirements to compile the code are a modern C++ compiler and the Boost C++ libraries.
Автор: Collica Randall S. Название: Customer Segmentation and Clustering Using SAS Enterprise Miner, Third Edition ISBN: 1642953091 ISBN-13(EAN): 9781642953091 Издательство: Неизвестно Рейтинг: Цена: 105390.00 T Наличие на складе: Невозможна поставка. Описание: Understanding your customers is the key to your company's success
Segmentation is one of the first and most basic machine learning methods. It can be used by companies to understand their customers better, boost relevance of marketing messaging, and increase efficacy of predictive models. In Customer Segmentation and Clustering Using SAS Enterprise Miner, Third Edition, Randy Collica explains, in step-by-step fashion, the most commonly available techniques for segmentation using the powerful data mining software SAS Enterprise Miner.
A working guide that uses real-world data, this new edition will show you how to segment customers more intelligently and achieve the one-to-one customer relationship that your business needs. Step-by-step examples and exercises, using a number of machine learning and data mining techniques, clearly illustrate the concepts of segmentation and clustering in the context of customer relationship management.
The book includes four parts, each of which increases in complexity. Part 1 reviews the basics of segmentation and clustering at an introductory level, providing examples from a variety of industries. Part 2 offers an in-depth treatment of segmentation with practical topics, such as when and how to update your models. Part 3 goes beyond traditional segmentation practices to introduce recommended strategies for clustering product affinities, handling missing data, and incorporating textual records into your predictive model with SAS Text Miner. Finally, part 4 takes segmentation to a new level with advanced techniques, such as clustering of product associations, developing segmentation-scoring models from customer survey data, combining segmentations using ensemble segmentation, and segmentation of customer transactions.
New to the third edition is a chapter that focuses on predictive models within microsegments and combined segments, and a new parallel process technique is introduced using SAS Factory Miner. In addition, all examples have been updated to the latest version of SAS Enterprise Miner.
Автор: Tin-Chih Toly Chen; Katsuhiro Honda Название: Fuzzy Collaborative Forecasting and Clustering ISBN: 3030225739 ISBN-13(EAN): 9783030225735 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book introduces the basic concepts of fuzzy collaborative forecasting and clustering, including its methodology, system architecture, and applications. It demonstrates how dealing with disparate data sources is becoming more and more popular due to the increasing spread of internet applications. The book proposes the concepts of collaborative computing intelligence and collaborative fuzzy modeling, and establishes several so-called fuzzy collaborative systems. It shows how technical constraints, security issues, and privacy considerations often limit access to some sources. This book is a valuable source of information for postgraduates, researchers and fuzzy control system developers, as it presents a very effective fuzzy approach that can deal with disparate data sources, big data, and multiple expert decision making.
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