| Description |
1 online resource (viii, 191 pages) : illustrations (some color) |
| Contents |
Intro; Contents; About the Editors; On Data Mining and Social Networking; A Review of Recommender System and Related Dimensions; 1 Introduction; 1.1 Motivation and Problem Explanation; 2 Literature Review; 3 Recommender System Model; 4 Evaluation Metrics for Recommendation Algorithms; 4.1 For Predict on User Ratings; 5 Dimensions of Recommender System; 6 Conclusion; References; Collaborative Filtering Techniques in Recommendation Systems; 1 Introduction; 2 Goals and Critical Challenges; 2.1 Goals; 2.2 Challenges; 3 Classification; 3.1 Content-Based Filtering System |
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2 Proposed Work2.1 System Overview; 2.2 Methodology; 2.3 Proposed Algorithm; 3 Results Analysis; 3.1 Precision; 3.2 Recall; 3.3 F-measures; 3.4 Time Requirements; 3.5 Memory Usage; 4 Conclusion and Future Work; 4.1 Conclusion; 4.2 Future Work; References; Sentiment Analysis on WhatsApp Group Chat Using R; 1 Introduction; 2 Literature Review; 3 Implementation of Sentiment Analysis Using R Studio; 4 Result Analysis; 5 Conclusion; References; A Recent Survey on Information-Hiding Techniques; 1 Introduction; 1.1 Information Hiding; 2 Illustration of Data-Hiding Technique |
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2.1 Survey on Reversible Data-Hiding Technique3 Comparison and Discussion; 4 Conclusion; References; Investigation of Feature Selection Techniques on Performance of Automatic Text Categorization; 1 Introduction; 2 Related Work; 3 Material and Methodology; 3.1 Data Source; 3.2 Methodology; 4 Experimental Results and Discussions; 5 Conclusion; References; Identification and Analysis of Future User Interactions Using Some Link Prediction Methods in Social Networks; 1 Introduction; 2 Related Work; 3 Methodology; 3.1 Overview; 3.2 Followers Matrix Computation; 3.3 Celebrity Data Removal |
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3.4 Positive Edges Sampling3.5 Negative Edges Generation and Sampling; 3.6 Feature Set Extraction; 3.7 Proximity Feature; 3.8 Ego-Centric Features; 3.9 Aggregation Features; 3.10 Edges Classification; 4 Unsupervised Learning; 4.1 Cosine Similarity; 4.2 Jaccard Similarity Coefficient; 4.3 Adamic-Adar Index; 5 Supervised Learning; 6 KNN; 6.1 Random Forest; 6.2 Non-linear SVM; 7 Experimental Results and Analysis; 8 Conclusion and Future Works; References; Sentiment Prediction of Facebook Status Updates of Youngsters; 1 Introduction; 2 Literature Review; 3 Proposed Methodology |
| Summary |
This book presents a compilation of current trends, technologies, and challenges in connection with Big Data. Many fields of science and engineering are data-driven, or generate huge amounts of data that are ripe for the picking. There are now more sources of data than ever before, and more means of capturing data. At the same time, the sheer volume and complexity of the data have sparked new developments, where many Big Data problems require new solutions. Given its scope, the book offers a valuable reference guide for all graduate students, researchers, and scientists interested in exploring the potential of Big Data applications |
| Notes |
Online resource; title from PDF title page (SpringerLink, viewed March 27, 2019) |
| Subject |
Data mining.
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Machine learning.
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Data Mining
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Machine Learning
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Data mining
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Machine learning
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| Form |
Electronic book
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| Author |
Shukla, Rajesh K., editor
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Agrawal, Jitendra, editor
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Sharma, Sanjeev (Information technology executive), editor.
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Tomer, Geetam Singh, editor
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| ISBN |
9789811363474 |
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9811363471 |
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9789811363481 |
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981136348X |
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9789811363498 |
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9811363498 |
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