Tag Archives: Clustering

Survey of Text Mining: Clustering, Classification, and Retrieval by Michael W. Berry

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Survey of Text Mining: Clustering, Classification, and Retrieval by Michael W. Berry
English | Sep 9, 2003 | ISBN: 0387955631 | 262 Pages | PDF | 5 MB
Extracting content from text continues to be an important research problem for information processing and management. Approaches to capture the semantics of text-based document collections may be based on Bayesian models, probability theory, vector space models, statistical models, or even graph theory.

Survey of Text Mining: Clustering, Classification, and Retrieval by Michael W. Berry
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Survey of Text Mining: Clustering, Classification, and Retrieval by Michael W. Berry

FREEDownload : Survey of Text Mining: Clustering, Classification, and Retrieval by Michael W. Berry

Survey of Text Mining: Clustering, Classification, and Retrieval by Michael W. Berry
English | Sep 9, 2003 | ISBN: 0387955631 | 262 Pages | PDF | 5 MB
Extracting content from text continues to be an important research problem for information processing and management. Approaches to capture the semantics of text-based document collections may be based on Bayesian models, probability theory, vector space models, statistical models, or even graph theory.

Survey of Text Mining: Clustering, Classification, and Retrieval by Michael W. Berry
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“Video Search and Mining” ed. by Dan Schonfeld, Caifeng Shan, Dacheng Tao, and LiangWang

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"Video Search and Mining" ed. by Dan Schonfeld, Caifeng Shan, Dacheng Tao, and LiangWang
Studies in Computational Intelligence, 287
Springer | 2010 | ISBN: 3642128998 9783642129001 9783642128998 | 386 pages | PDF | 7 MB
This book provides an overview of emerging new approaches to video search and mining based on promising methods being developed in the computer vision and image analysis community. The objective of this book is to present the latest advances in video search and mining covering both theoretical approaches and practical applications.

“Video Search and Mining” ed. by Dan Schonfeld, Caifeng Shan, Dacheng Tao, and LiangWang
Video search and mining is a rapidly evolving discipline whose aim is to capture interesting patterns in video data. It has become one of the core areas in the data mining research community. In comparison to other types of data mining (e.g. text), video mining is still in its infancy.
The book provides researchers and practitioners a comprehensive understanding of the start-of-the-art in video search and mining techniques and a resource for potential applications and successful practice.
This book can also serve as an important reference tool and handbook for researchers and practitioners in video search and mining.

Contents
Preface
Object Trajectory Analysis in Video Indexing and Retrieval Applications
Trajectory Clustering for Scene Context Learning and Outlier Detection
Motion Trajectory-Based Video Retrieval, Classification, and Summarization
Three Dimensional Information Extraction and Applications to Video Analysis
Statistical Analysis on Manifolds and Its Applications to Video Analysis*
Semantic Video Content Analysis
Video Genre Inference Based on Camera Capturing Models
Visual Concept Learningfrom Weakly Labeled Web Videos
Face Recognition and Retrieval in Video
A Human-Centered Computing Framework to Enable Personalized News Video Recommendation
A Holistic, In-Compression Approach to Mininglndependent Motion Segments for Massive Surveillance Video Collections
Video Repeat Recognition and Mining by Visual Features
Mining TV Broadcasts 24/7 for Recurring Video Sequences
YouTube Scale, Large Vocabulary Video Annotation
Author Index
1st with true TOC BookMarkLinks
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“Advances in Electrocardiograms: Methods and Analysis” ed. by Richard M. Millis

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"Advances in Electrocardiograms: Methods and Analysis" ed. by Richard M. Millis
InTeOpP | 2012 | ISBN: 9533079231 9789533079233 | 401 pages | PDF | 20 MB
This volume lays the groundwork for understanding the technical aspects of these advancements. This volume is complemented with anatomical diagrams, electrocardiogram recordings, flow diagrams and algorithms which demonstrate the most modern principles of electrocardiography. The five sections of this volume, Cardiac Anatomy, ECG Technique, ECG Features, Heart Rate Variability and ECG Data Management, provide comprehensive reviews of advancements in the technical and analytical methods for interpreting and evaluating electrocardiograms.

“Advances in Electrocardiograms: Methods and Analysis” ed. by Richard M. Millis
The chapters which form this volume describe how the technical impediments inherent to instrument-patient interfacing, recording and interpreting variations in electrocardiogram time intervals and morphologies, as well as electrocardiogram data sharing have been effectively overcome. The advent of novel detection, filtering and testing devices are described. Foremost, among these devices are innovative algorithms for automating the evaluation of electrocardiograms.

Contents
Part 1 Cardiac Structure and Function
1 Cardiac Anatomy
Part 2 ECG Technique
2 Low-Frequency Response and the Skin-Electrode Interface in Dry-Electrode Electrocardiography
3 Implantation Techniques of Leads for Left Ventricular Pacing in Cardiac Resynchronization Therapy and Electrocardiographic Consequences of the Stimulation Site
4 Non Contact Heart Monitoring
5 Automated Selection of Optimal ECG Lead Using Heart Instantaneous Frequency During Sleep
Part 3 ECG Feature Analysis
6 A Novel Technique for ECG Morphology Interpretation and Arrhythmia Detection Based on Time Series Signal Extracted from Scanned ECG Record
7 QT Interval and QT Variability
8 The Electrocardiogram – Waves and Intervals
9 Quantification of Ventricular Repolarization Dispersion Using Digital Processing of the Surface ECG
10 Medicines and QT Prolongation
11 Concealed Conduction
12 Recognition of Cardiac Arrhythmia by Means of Beat Clustering on ECG-Holter Recordings
Part 4 Heart Rate Variability
13 Electrocardiographic Analysis of Heart Rate Variability in Aging Heart
14 Changes of Sympathovagal Balance Measured by Heart Rate Variability in Gastroparetic Patients Treated with Gastric Electrical Stimulation
15 Associations of Metabolic Variables with Electrocardiographic Measures of Sympathovagal Balance in Healthy Young Adults
Part 5 ECG Signal Processing
16 An Analogue Front-End System with a Low-Power On-Chip Filter and ADC for Portable ECG Detection Devices
17 Electrocardiogram in an MRI Environment: Clinical Needs. Practical Considerations. Safety Implications. Technical Solutions and Future Directions
18 Customized Heart Check System by Using Integrated Information of Electrocardiogram and Plethysmogram Outside the Driver's Awareness from an Automobile Steering Wheel
19 Independent Component Analysis in ECG Signal Processing
Part 6 ECG Data Management
20 Broadening the Exchange of Electrocardiogram Data from Intra-Hospital to Inter-Hospital
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Chaotic Synchronization: Applications to Living Systems

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Erik Mosekilde, "Chaotic Synchronization: Applications to Living Systems"
English | ISBN: 9810247893 | 2002 | 440 pages | PDF | 30 MB

Chaotic Synchronization: Applications to Living Systems
Interacting chaotic oscillators are of interest in many areas of physics, biology, and engineering. In the biological sciences, for instance, one of the challenging problems is to understand how a group of cells or functional units, each displaying complicated nonlinear dynamic phenomena, can interact with one another to produce a coherent response on a higher organizational level. This book is a guide to the concept of chaotic synchronization. The topics covered range from transverse stability and riddled basins of attraction in a system of two coupled logistic maps, over partial synchronization and clustering in systems of many chaotic oscillators, to noise-induced synchronization of coherence resonance oscillators. Other topics treated in the book are on-off intermittency and the role of the absorbing and mixed absorbing areas, periodic orbit threshold theory, the influence of a small parameter mismatch, and different mechanisms for chaotic phase synchronization. The biological examples include synchronization of the bursting behaviour of coupled insulin-producing beta cells, chaotic phase synchronization in the pressure and flow regulation of neighbouring functional units of the kidney, and homoclinic transitions to phase synchronization in microbiological reactors. This book should be of interest to students and researchers interested in applying new concepts of chaotic synchronization and clustering to biological systems.
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Chaotic Synchronization: Applications to Living Systems

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Erik Mosekilde, "Chaotic Synchronization: Applications to Living Systems"
English | ISBN: 9810247893 | 2002 | 440 pages | PDF | 30 MB

Chaotic Synchronization: Applications to Living Systems
Interacting chaotic oscillators are of interest in many areas of physics, biology, and engineering. In the biological sciences, for instance, one of the challenging problems is to understand how a group of cells or functional units, each displaying complicated nonlinear dynamic phenomena, can interact with one another to produce a coherent response on a higher organizational level. This book is a guide to the concept of chaotic synchronization. The topics covered range from transverse stability and riddled basins of attraction in a system of two coupled logistic maps, over partial synchronization and clustering in systems of many chaotic oscillators, to noise-induced synchronization of coherence resonance oscillators. Other topics treated in the book are on-off intermittency and the role of the absorbing and mixed absorbing areas, periodic orbit threshold theory, the influence of a small parameter mismatch, and different mechanisms for chaotic phase synchronization. The biological examples include synchronization of the bursting behaviour of coupled insulin-producing beta cells, chaotic phase synchronization in the pressure and flow regulation of neighbouring functional units of the kidney, and homoclinic transitions to phase synchronization in microbiological reactors. This book should be of interest to students and researchers interested in applying new concepts of chaotic synchronization and clustering to biological systems.
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Scikit-Learn Cookbook

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Scikit-Learn Cookbook by Trent Hauck
2014 | ISBN: 1783989483 | English | 214 pages | PDF | 3 MB

Scikit-Learn Cookbook
Over 50 recipes to incorporate scikit-learn into every step of the data science pipeline, from feature extraction to model building and model evaluation

About This Book

Learn how to handle a variety of tasks with Scikit-Learn with interesting recipes that show you how the library really works
Use Scikit-Learn to simplify the programming side data so you can focus on thinking
Discover how to apply algorithms in a variety of situations
Who This Book Is For

If you're a data scientist already familiar with Python but not Scikit-Learn, or are familiar with other programming languages like R and want to take the plunge with the gold standard of Python machine learning libraries, then this is the book for you.

In Detail

Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility, and within the Python data space, scikit-learn is the unequivocal choice for machine learning. Its consistent API and plethora of features help solve any machine learning problem it comes across.

The book starts by walking through different methods to prepare your data—be it a dataset with missing values or text columns that require the categories to be turned into indicator variables. After the data is ready, you'll learn different techniques aligned with different objectives—be it a dataset with known outcomes such as sales by state, or more complicated problems such as clustering similar customers. Finally, you'll learn how to polish your algorithm to ensure that it's both accurate and resilient to new datasets.
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Scikit-Learn Cookbook

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Scikit-Learn Cookbook by Trent Hauck
2014 | ISBN: 1783989483 | English | 214 pages | PDF | 3 MB

Scikit-Learn Cookbook
Over 50 recipes to incorporate scikit-learn into every step of the data science pipeline, from feature extraction to model building and model evaluation

About This Book

Learn how to handle a variety of tasks with Scikit-Learn with interesting recipes that show you how the library really works
Use Scikit-Learn to simplify the programming side data so you can focus on thinking
Discover how to apply algorithms in a variety of situations
Who This Book Is For

If you're a data scientist already familiar with Python but not Scikit-Learn, or are familiar with other programming languages like R and want to take the plunge with the gold standard of Python machine learning libraries, then this is the book for you.

In Detail

Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility, and within the Python data space, scikit-learn is the unequivocal choice for machine learning. Its consistent API and plethora of features help solve any machine learning problem it comes across.

The book starts by walking through different methods to prepare your data—be it a dataset with missing values or text columns that require the categories to be turned into indicator variables. After the data is ready, you'll learn different techniques aligned with different objectives—be it a dataset with known outcomes such as sales by state, or more complicated problems such as clustering similar customers. Finally, you'll learn how to polish your algorithm to ensure that it's both accurate and resilient to new datasets.
Download links
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Scikit-Learn Cookbook (PDF)

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Scikit-Learn Cookbook by Trent Hauck
2014 | ISBN: 1783989483 | English | 214 pages | PDF | 3 MB
Over 50 recipes to incorporate scikit-learn into every step of the data science pipeline, from feature extraction to model building and model evaluation

Scikit-Learn Cookbook (PDF)
About This Book

Learn how to handle a variety of tasks with Scikit-Learn with interesting recipes that show you how the library really works
Use Scikit-Learn to simplify the programming side data so you can focus on thinking
Discover how to apply algorithms in a variety of situations
Who This Book Is For

If you're a data scientist already familiar with Python but not Scikit-Learn, or are familiar with other programming languages like R and want to take the plunge with the gold standard of Python machine learning libraries, then this is the book for you.

In Detail

Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility, and within the Python data space, scikit-learn is the unequivocal choice for machine learning. Its consistent API and plethora of features help solve any machine learning problem it comes across.

The book starts by walking through different methods to prepare your data—be it a dataset with missing values or text columns that require the categories to be turned into indicator variables. After the data is ready, you'll learn different techniques aligned with different objectives—be it a dataset with known outcomes such as sales by state, or more complicated problems such as clustering similar customers. Finally, you'll learn how to polish your algorithm to ensure that it's both accurate and resilient to new datasets.

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