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Artificial Intelligence Applications and Innovations

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Table of Contents

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    Book Overview
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    Chapter 1 A Probabilistic Approach to Organic Component Detection in Leishmania Infected Microscopy Images
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    Chapter 2 Combination of M-Estimators and Neural Network Model to Analyze Inside/Outside Bark Tree Diameters
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    Chapter 3 Artificial Intelligence Applications and Innovations
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    Chapter 4 Support Vector Machine Classification of Protein Sequences to Functional Families Based on Motif Selection
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    Chapter 5 A Multi-objective Genetic Algorithm for Software Development Team Staffing Based on Personality Types
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    Chapter 6 Artificial Intelligence Applications and Innovations
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    Chapter 7 Fine Tuning of a Wet Clutch Engagement by Means of a Genetic Algorithm
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    Chapter 8 A Representational MDL Framework for Improving Learning Power of Neural Network Formalisms
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    Chapter 9 On the Design and Training of Bots to Play Backgammon Variants
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    Chapter 10 Surrogate Modelling of Solutions of Integral Equations by Neural Networks
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    Chapter 11 A Regularization Network Committee Machine of Isolated Regularization Networks for Distributed Privacy Preserving Data Mining
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    Chapter 12 Improved POS-Tagging for Arabic by Combining Diverse Taggers
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    Chapter 13 Multithreaded Implementation of the Slope One Algorithm for Collaborative Filtering
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    Chapter 14 Experimental Identification of Pilot Response Using Measured Data from a Flight Simulator
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    Chapter 15 A Probabilistic Knowledge-Based Information System for Environmental Policy Modeling and Decision Making
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    Chapter 16 Conceptualization and Significance Study of a New Appliation CS-MIR
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    Chapter 17 Physical Bongard Problems
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    Chapter 18 Taxonomy Development and Its Impact on a Self-learning e-Recruitment System
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    Chapter 19 Fuzzy Energy-Based Active Contours Exploiting Local Information
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    Chapter 20 Fuzzy Friction Modeling for Adaptive Control of Mechatronic Systems
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    Chapter 21 Fuzzy Graph Language Recognizability
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    Chapter 22 Adaptive Intuitionistic Fuzzy Inference Systems of Takagi-Sugeno Type for Regression Problems
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    Chapter 23 A Hybrid Method for Evaluating Biomass Suppliers – Use of Intuitionistic Fuzzy Sets and Multi-Periodic Optimization
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    Chapter 24 A Neural Network for Spatial and Temporal Modeling of foF2 Data Based on Satellite Measurements
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    Chapter 25 Detecting Glycosylations in Complex Samples
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    Chapter 26 Experiments with Face Recognition Using a Novel Approach Based on CVQ Technique
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    Chapter 27 Novel Matching Methods for Automatic Face Recognition Using SIFT
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    Chapter 28 Exploring the Design Space of a Declarative Framework for Automated Negotiation: Initial Considerations
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    Chapter 29 Hybrid and Reinforcement Multi Agent Technology for Real Time Air Pollution Monitoring
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    Chapter 30 Rule-Based Behavior Prediction of Opponent Agents Using Robocup 3D Soccer Simulation League Logfiles
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    Chapter 31 An Ontology-Based Model for Student Representation in Intelligent Tutoring Systems for Distance Learning
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    Chapter 32 Assistant Tools for Teaching FOL to CF Conversion
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    Chapter 33 Effective Diagnostic Feedback for Online Multiple-Choice Questions
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    Chapter 34 A Fast Hybrid k -NN Classifier Based on Homogeneous Clusters
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    Chapter 35 A Spatially-Constrained Normalized Gamma Process for Data Clustering
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    Chapter 36 GamRec: A Clustering Method Using Geometrical Background Knowledge for GPR Data Preprocessing
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    Chapter 37 Enhancing Clustering by Exploiting Complementary Data Modalities in the Medical Domain
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    Chapter 38 Extraction of Web Image Information: Semantic or Visual Cues?
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    Chapter 39 Trust-Aware Clustering Collaborative Filtering: Identification of Relevant Items
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    Chapter 40 Unsupervised Detection of Fibrosis in Microscopy Images Using Fractals and Fuzzy c-Means Clustering
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    Chapter 41 Image Threshold Selection Exploiting Empirical Mode Decomposition
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    Chapter 42 Modelling Crowdsourcing Originated Keywords within the Athletics Domain
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    Chapter 43 Scalable Object Encoding Using Multiplicative Multilinear Inter-camera Prediction in the Context of Free View 3D Video
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    Chapter 44 Correlation between Seismic Intensity Parameters of HHT-Based Synthetic Seismic Accelerograms and Damage Indices of Buildings
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    Chapter 45 Improving Current and Voltage Transformers Accuracy Using Artificial Neural Network
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    Chapter 46 Modeling of Syllogisms in Analog Hardware
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    Chapter 47 A New Approach to High Impedance Fault Detection Based on Correlation Functions
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    Chapter 48 Network Selection in a Virtual Network Operator Environment
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    Chapter 49 Position and Velocity Predictions of the Piston in a Wet Clutch System during Engagement by Using a Neural Network Modeling
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    Chapter 50 Uniform Asymptotic Stability and Global Asymptotic Stability for Time-Delay Hopfield Neural Networks
Attention for Chapter 37: Enhancing Clustering by Exploiting Complementary Data Modalities in the Medical Domain
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Chapter title
Enhancing Clustering by Exploiting Complementary Data Modalities in the Medical Domain
Chapter number 37
Book title
Artificial Intelligence Applications and Innovations
Published in
IFIP advances in information and communication technology, September 2012
DOI 10.1007/978-3-642-33409-2_37
Pubmed ID
Book ISBNs
978-3-64-233408-5, 978-3-64-233409-2
Authors

Samah Jamal Fodeh, Ali Haddad, Cynthia Brandt, Martin Schultz, Michael Krauthammer

Abstract

Data Clustering has been an active area of research in many different application areas, with existing clustering algorithms mostly focusing on partitioning one modality or representation of the data. In this study, we delineate and demonstrate a new, enhanced data clustering approach whose innovation is its exploitation of multiple data modalities. We propose BI-NMF, a bi-modal clustering approach based on Non Negative Matrix Factorization (NMF) that clusters two differing data modalities simultaneously. The strength of our approach is its combining of multiple aspects of the data when forming the final clusters. To assess the utility of our approach, we performed several experiments on two distinct biomedical datasets with two modalities each. Comparing the clusters of BI-NMF with NMF clusters of single data modality, we observed consistent performance enhancement across both datasets. Our experimental results suggest that BI-NMF is advantageous for boosting data clustering.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 33%
Other 1 17%
Unknown 3 50%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 2 33%
Computer Science 1 17%
Unknown 3 50%