What are hot research topics in data mining in 2017?

1. Deep Learning and Neural Networks:

   • Deep learning techniques, including neural networks, were gaining prominence in data mining research. Researchers were exploring applications of deep learning in various domains, such as image recognition, natural language processing, and pattern recognition.

2. Big Data Analytics:

   • With the growing volume of data, researchers were focusing on developing scalable and efficient algorithms for big data analytics. This involved exploring distributed computing frameworks, parallel processing, and innovative data storage solutions.

3. Spatial Data Mining:

   • Spatial data mining, dealing with geographical and location-based data, was an active area of research. Applications included urban planning, environmental monitoring, and location-based services.

4. Graph Mining and Network Analysis:

   • Research in graph mining and network analysis was gaining attention. This involved studying complex relationships and patterns in networks, such as social networks, biological networks, and transportation networks.

5. Privacy-Preserving Data Mining:

   • With increasing concerns about privacy, researchers were working on developing techniques for privacy-preserving data mining. This included methods for anonymization, encryption, and secure multi-party computation.

6. Stream Data Mining:

   • Streaming data, generated in real-time, posed new challenges for data mining. Researchers were exploring algorithms and techniques to analyze and extract patterns from continuous streams of data.

7. Text and Sentiment Analysis:

   • Analyzing text data, including sentiment analysis and opinion mining, was an active area of research. Researchers were developing methods to extract insights from large volumes of unstructured textual data.

8. Healthcare Data Mining:

   • There was a focus on applying data mining techniques in healthcare for tasks such as disease prediction, patient monitoring, and personalized medicine. Researchers were exploring ways to utilize electronic health records and biomedical data.

9. Time Series Data Mining:

   • Analyzing time series data presented challenges in terms of pattern recognition and prediction. Researchers were exploring methods for efficient time series data mining in various domains, including finance and environmental monitoring.

10. Ensemble Learning:

    • Ensemble learning, which involves combining multiple models to improve predictive performance, was a popular research topic. Researchers were investigating novel ensemble methods and their applications in different domains.

It's important to note that research trends can evolve, and new topics may emerge over time. If you are interested in the latest research topics in data mining, I recommend checking recent conference proceedings, journals, and publications in the field for up-to-date information.