Preface; Organizing Committee; Patrons; Conference Chair; Conference Co-Chairs; Convenors; Co-Convenors; Conference Committee; Publicity Chair International; Publicity Chair National; Program and Publication Chair; Accommodation Committee; Advisory Board-International/National, Technical Program Committee; A Note from the Organizing Committee; Contents; About the Editors; 1 Comparative Study of Techniques and Issues in Data Clustering; Abstract; 1 Introduction; 2 Clustering in Data Mining; 2.1 Partitioning Clustering; 2.2 Density Based Clustering; 2.3 Model Based Clustering

2.4 Hierarchical Clustering3 Literature Survey; 3.1 Identification of Formation of Clusters; 3.2 Clustering Large Datasets; 3.3 Large Computational Time; 3.4 Efficient Initial Seed Selection; 3.5 Identification of Different Distance and Similarity Measures; 4 Summary of Clustering Approaches; 5 Conclusion; References; 2 Adaptive Pre-processing and Regression of Weather Data; Abstract; 1 Introduction; 2 Related Work; 3 Proposed Method; 4 Experimental Results; 5 Conclusion and Future Scope; References; 3 A Comparative Analysis for CBIR Using Fast Discrete Curvelet Transform; Abstract

1 Introduction1.1 Content Based Image Retrieval (CBIR); 2 Fast Discrete Curvelet Transform; 2.1 Curvelet Computation; 3 Implementation; 3.1 General Idea; 3.2 Detailed Description; 4 Results and Analysis; 4.1 Outputs; 4.2 Analysis; 5 Conclusion and Future Scope; References; 4 Compute the Requirements and Need of an Online Donation Platform for Non-monetary Resources Using Statistical Analyses; Abstract; 1 Introduction; 2 Literature Review; 2.1 Existing Frameworks Regarding Donor Behavior; 2.2 Marketing Activities of NGO Influencing Donor's Perception

2.3 Applying the Donor Knowledge to an Online Solution3 Result Analysis; 3.1 T-Test; 3.2 ANOVA Test; 3.3 Summary of Results; 4 Conclusion; References; 5 Enacting Segmentation Algorithms for Classifying Fish Species; Abstract; 1 Introduction; 1.1 Nearest Neighbor Classifier; 1.2 Watershed Algorithm; 2 Implementing Watershed Algorithm; 2.1 Load Image; 2.2 Morphological Transformation; 2.3 Adjustment; 2.4 Converting Image to Black and White; 2.5 Calculating BW Distance; 2.6 Applying Watershed; 3 Implementing Nearest Neighbor Classifier; 3.1 Load Image; 3.2 Initialize the Storage for Each Sample

3.3 Selecting Each Sample Region3.4 Converting rgb to l*a*b* Image; 3.5 Calculation of Mean a* and b* Values; 3.6 Performance Classification; 3.7 Clearing Value Distance; 4 Results and Discussions; 4.1 Watershed Algorithm; 4.2 Nearest Neighbor Classifier Algorithm; 5 Conclusion; References; 6 Pattern Based Extraction of Times from Natural Language Text; Abstract; 1 Introduction; 2 Existing Works; 3 Frame Work for Times Extraction; 3.1 Components Description; 4 Pattern Based Rules for Times Extraction; 5 Results; 6 Conclusion and Future Work; References