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Preface; ISDA 2016
Organization; General Chairs; Program Chairs; Advisory Board members; Tutorials/Workshops and Special Sessions Chairs; Publication Chair; Web Master; Local Organizing Committee; Technical Program Committee; Contents; An Innovative Approach to Manage Heterogeneous Information Using Relational Database Systems; Abstract; 1 Introduction; 2 Defining the Concepts Used for Solving this Problem; 2.1 Library Standards for Defining Information; 2.2 Representing Information Structures Using FRBR Model; 3 Problem Solution Using Metadata Definitions; 4 Preliminary Results
5 ConclusionsReferences; Reliable Attribute Selection Based on Random Forest (RASER); Abstract; 1 Introduction; 2 Related Work; 3 Reliable Attribute Selection Based on Random Forest (RASER); 3.1 Measuring Features Relevance; 3.2 Measuring Redundancy; 3.3 Subgraph Computation; 4 Experimental Results; 4.1 Value of Relevance; 4.2 Performance; 5 Conclusion; References; Estimating the Number of Clusters as a Pre-processing Step to Unsupervised Learning; Abstract; 1 Introduction; 2 Estimating the Number of Clusters of a Clustering by Using a Sequential Clustering Algorithm; 3 The BSAS Algorithm
4 Data Domains for the Experiments5 Experiments, Results and Analysis; 6 Conclusions; Acknowledgments; References; Agglomerative and Divisive Approaches to Unsupervised Learning in Gestalt Clusters; Abstract; 1 Introduction; 2 The Agglomerative Approach; 3 The Divisive Approach; 4 Data Domain for the Experiments; 5 Experiments, Results and Analysis; 6 Conclusions; Acknowledgments; References; Improving Imputation Accuracy in Ordinal Data Using Classification; 1 Introduction; 2 Related Work; 3 Classifier Based Nominal Imputation Algorithm (CNI); 4 Experimental Setup; 4.1 Experimental Results
4.2 Classification Accuracy4.3 K Nearest Neighbour Algorithm (KNN-5); 5 Conclusions; References; GA-PSO-FASTSLAM: A Hybrid Optimization Approach in Improving FastSLAM Performance; Abstract; 1 Introduction; 2 Background; 2.1 GA-FastSLAM Approach; 2.2 PSO-FastSLAM Approach; 3 The Proposed GA-PSO-FastSLAM Approach; 4 Experiment; 4.1 Experiment Setup; 4.2 Experiment Result; 5 Conclusion; Acknowledgment; References; Three Case Studies Using Agglomerative Clustering; Abstract; 1 Introduction; 2 AGNES (AGglomerative NESting) Algorithm; 3 Data Description
4 Validation Indices, Experiments, Results and Analysis5 Conclusions; Acknowledgments; References; Historic Document Image De-noising Using Principal Component Analysis (PCA) and Local Pixel Grouping ...; Abstract; 1 Introduction; 2 Related Work; 3 LPG-PCA De-noising Algorithm; 3.1 Principal Component Analysis (PCA); 3.2 Local Pixel Grouping (LPG); 3.3 LPG-PCA Based De-noising First Stage; 3.4 LPG-PCA Based De-noising Refinement in Second Stage; 4 Experimentation; 4.1 Datasets; 4.2 Assessment Metrics; 4.3 Experimental Result; 5 Conclusion; Acknowledgments; References
Organization; General Chairs; Program Chairs; Advisory Board members; Tutorials/Workshops and Special Sessions Chairs; Publication Chair; Web Master; Local Organizing Committee; Technical Program Committee; Contents; An Innovative Approach to Manage Heterogeneous Information Using Relational Database Systems; Abstract; 1 Introduction; 2 Defining the Concepts Used for Solving this Problem; 2.1 Library Standards for Defining Information; 2.2 Representing Information Structures Using FRBR Model; 3 Problem Solution Using Metadata Definitions; 4 Preliminary Results
5 ConclusionsReferences; Reliable Attribute Selection Based on Random Forest (RASER); Abstract; 1 Introduction; 2 Related Work; 3 Reliable Attribute Selection Based on Random Forest (RASER); 3.1 Measuring Features Relevance; 3.2 Measuring Redundancy; 3.3 Subgraph Computation; 4 Experimental Results; 4.1 Value of Relevance; 4.2 Performance; 5 Conclusion; References; Estimating the Number of Clusters as a Pre-processing Step to Unsupervised Learning; Abstract; 1 Introduction; 2 Estimating the Number of Clusters of a Clustering by Using a Sequential Clustering Algorithm; 3 The BSAS Algorithm
4 Data Domains for the Experiments5 Experiments, Results and Analysis; 6 Conclusions; Acknowledgments; References; Agglomerative and Divisive Approaches to Unsupervised Learning in Gestalt Clusters; Abstract; 1 Introduction; 2 The Agglomerative Approach; 3 The Divisive Approach; 4 Data Domain for the Experiments; 5 Experiments, Results and Analysis; 6 Conclusions; Acknowledgments; References; Improving Imputation Accuracy in Ordinal Data Using Classification; 1 Introduction; 2 Related Work; 3 Classifier Based Nominal Imputation Algorithm (CNI); 4 Experimental Setup; 4.1 Experimental Results
4.2 Classification Accuracy4.3 K Nearest Neighbour Algorithm (KNN-5); 5 Conclusions; References; GA-PSO-FASTSLAM: A Hybrid Optimization Approach in Improving FastSLAM Performance; Abstract; 1 Introduction; 2 Background; 2.1 GA-FastSLAM Approach; 2.2 PSO-FastSLAM Approach; 3 The Proposed GA-PSO-FastSLAM Approach; 4 Experiment; 4.1 Experiment Setup; 4.2 Experiment Result; 5 Conclusion; Acknowledgment; References; Three Case Studies Using Agglomerative Clustering; Abstract; 1 Introduction; 2 AGNES (AGglomerative NESting) Algorithm; 3 Data Description
4 Validation Indices, Experiments, Results and Analysis5 Conclusions; Acknowledgments; References; Historic Document Image De-noising Using Principal Component Analysis (PCA) and Local Pixel Grouping ...; Abstract; 1 Introduction; 2 Related Work; 3 LPG-PCA De-noising Algorithm; 3.1 Principal Component Analysis (PCA); 3.2 Local Pixel Grouping (LPG); 3.3 LPG-PCA Based De-noising First Stage; 3.4 LPG-PCA Based De-noising Refinement in Second Stage; 4 Experimentation; 4.1 Datasets; 4.2 Assessment Metrics; 4.3 Experimental Result; 5 Conclusion; Acknowledgments; References