Data mining and machine learning applications /
edited by Rohit Raja [and more].
- Hoboken, NJ : Beverly, MA : Wiley ; Scrivener Publishing, 2022.
- 1 online resource
Includes bibliographical references and index.
Introduction to Data Mining / Classification and Mining Behavior of Data / A Comparative Overview of Hybrid Recommender Systems: Review, Challenges, and Prospects / Stream Mining: Introduction, Tools & Techniques and Applications / Data Mining Tools and Techniques: Clustering Analysis / Data Mining Implementation Process / Predictive Analytics in IT Service Management (ITSM) / Modified Cross-Sell Model for Telecom Service Providers Using Data Mining Techniques / Inductive Learning Including Decision Tree and Rule Induction Learning / Data Mining for Cyber-Physical Systems / Developing Decision Making and Risk Mitigation: Using CRISP-Data Mining / Human-Machine Interaction and Visual Data Mining / MSDTrA: A Boosting Based-Transfer Learning Approach for Class Imbalanced Skin Lesion Dataset for Melanoma Detection / New Algorithms and Technologies for Data Mining / Classification of EEG Signals for Detection of Epileptic Seizure Using Restricted Boltzmann Machine Classifier / An Enhanced Security of Women and Children Using Machine Learning and Data Mining Techniques / Santosh R Durugkar, Rohit Raja, Kapil Kumar Nagwanshi, and Ramakant Chandrakar. Santosh R Durugkar, Rohit Raja, Kapil Kumar Nagwanshi, and Sandeep Kumar -- Srinivas Konda, Kavitarani Balmuri, and Kishore Kumar Mamidala -- Rakhi Seth and Aakanksha Sharaff -- Naresh Kumar Nagwani -- Rohit Miri, Amit Kumar Dewangan, SR Tandan, and Priya Bhatnagar, Hiral Raja -- Kamal K Mehta, Rajesh Tiwari, and Nishant Behar -- Christa IL Sharon and V Suma -- K Ramya Laxmi, Sumit Srivastava, K Madhuravani, S Pallavi, and Omprakash Dewangan -- Raj Kumar Patra, A Mahendar, and G Madhukar -- M Varaprasad Rao, D Anji Reddy, Anusha Ampavathi, and Shaik Munawar -- Vivek Parganiha, Soorya Prakash Shukla, and Lokesh Kumar Sharma -- Sinha Upasana, Gupta Akanksha, Samera Khan, Shilpa Rani, and Swati Jain -- Lokesh Singh, Rekh Ram Janghe, and Satya Prakash Sahu -- Padma Bonde, Latika Pinjarkar, Korhan Cengiz, Aditi Shukla, and Maguluri Sudeep Joel -- Sudesh Kumar, Rekh Ram Janghel, and Satya Prakash Sahu -- Nanda R Wagh and Sanjay R Sutar -- Conclusion and Future Direction in Data Mining and Machine Learning /
Data, the latest currency of today's world, is the new gold. In this new form of gold, the most beautiful jewels are data analytics and machine learning. Data mining and machine learning are considered interdisciplinary fields. Data mining is a subset of data analytics and machine learning involves the use of algorithms that automatically improve through experience based on data. Massive datasets can be classified and clustered to obtain accurate results. The most common technologies used include classification and clustering methods. Accuracy and error rates are calculated for regression and classification and clustering to find actual results through algorithms like support vector machines and neural networks with forward and backward propagation. Applications include fraud detection, image processing, medical diagnosis, weather prediction, e-commerce and so forth. The book features: A review of the state-of-the-art in data mining and machine learning, A review and description of the learning methods in human-computer interaction, Implementation strategies and future research directions used to meet the design and application requirements of several modern and real-time applications for a long time, The scope and implementation of a majority of data mining and machine learning strategies. A discussion of real-time problems. Audience Industry and academic researchers, scientists, and engineers in information technology, data science and machine and deep learning, as well as artificial intelligence more broadly.
Data mining. Machine learning. Data Mining Machine Learning Exploration de donn�ees (Informatique) Apprentissage automatique. Data mining Machine learning