Advances in Knowledge Discovery and Data Mining 1st edition by Qiang Yang, Zhi Hua Zhou, Zhiguo Gong, Min Ling Zhang, Sheng Jun Huang ISBN 3030161455 9783030161453

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ISBN 10:      3030161455
ISBN 13: 978- 3030161453
Author:      Qiang Yang, Zhi-Hua Zhou, Zhiguo Gong, Min-Ling Zhang, Sheng-Jun Huang

The three-volume set LNAI 11439, 11440, and 11441 constitutes the thoroughly refereed proceedings of the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019, held in Macau, China, in April 2019.

The 137 full papers presented were carefully reviewed and selected from 542 submissions. The papers present new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, visualization, decision-making systems, and the emerging applications. They are organized in the following topical sections: classification and supervised learning; text and opinion mining; spatio-temporal and stream data mining; factor and tensor analysis; healthcare, bioinformatics and related topics; clustering and anomaly detection; deep learning models and applications; sequential pattern mining; weakly supervised learning; recommender system; social network and graph mining; data pre-processing and featureselection; representation learning and embedding; mining unstructured and semi-structured data; behavioral data mining; visual data mining; and knowledge graph and interpretable data mining.

Advances in Knowledge Discovery and Data Mining 1st Table of contents:

• Chapter 1: Introduction to Knowledge Discovery and Data Mining

  • Basics of Knowledge Discovery (KD) and Data Mining (DM)
  • Data Preprocessing and Transformation Techniques
  • Key Concepts in Data Mining Algorithms

• Chapter 2: Classification and Prediction

  • Overview of Classification Methods
  • Decision Trees, Support Vector Machines, and Neural Networks
  • Advanced Techniques in Predictive Modeling
  • Case Studies and Applications

• Chapter 3: Clustering and Association

  • Fundamentals of Clustering Algorithms
  • k-Means, DBSCAN, and Hierarchical Clustering
  • Association Rule Mining and Frequent Pattern Discovery
  • Applications in Market Basket Analysis

• Chapter 4: Dimensionality Reduction

  • The Need for Dimensionality Reduction
  • Principal Component Analysis (PCA)
  • Linear Discriminant Analysis (LDA)
  • Applications in Data Visualization and Preprocessing

• Chapter 5: Ensemble Learning

  • Introduction to Ensemble Methods
  • Bagging, Boosting, and Stacking Techniques
  • Random Forests, AdaBoost, and XGBoost
  • Enhancing Model Accuracy through Ensemble Approaches

• Chapter 6: Deep Learning and Neural Networks

  • Overview of Deep Learning Techniques
  • Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
  • Autoencoders and Generative Models
  • Applications of Deep Learning in Image and Text Analysis

• Chapter 7: Mining Text and Web Data

  • Text Mining and Natural Language Processing (NLP)
  • Web Mining and Social Media Data Mining
  • Sentiment Analysis, Topic Modeling, and Document Clustering
  • Applications in Content Analysis and Marketing

• Chapter 8: Mining Stream and Complex Data

  • Data Stream Mining Techniques
  • Handling Large-Scale, High-Velocity Data
  • Mining Complex Data Types such as Graphs and Time Series
  • Applications in Real-Time Analytics

• Chapter 9: Privacy-Preserving Data Mining

  • Privacy Concerns in Data Mining
  • Techniques for Privacy-Preserving Data Mining
  • Differential Privacy and Secure Multiparty Computation
  • Legal and Ethical Aspects in Data Mining

• Chapter 10: Data Mining in Bioinformatics

  • Applications of Data Mining in Genomics and Proteomics
  • Techniques for Mining Biological Data
  • Case Studies in Disease Prediction and Drug Discovery
  • Integrating Biological and Computational Models

• Chapter 11: Evaluation and Validation of Data Mining Models

  • Metrics for Evaluating Model Performance
  • Cross-Validation, Precision, Recall, and F1 Score
  • Overfitting and Underfitting in Model Training
  • Techniques for Model Improvement

• Chapter 12: Future Directions in Knowledge Discovery and Data Mining

  • Emerging Trends in Data Mining and Machine Learning
  • Integrating Artificial Intelligence with Data Mining
  • Future Challenges and Opportunities in Big Data Analytics
  • The Role of Knowledge Discovery in Decision Support Systems

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