DATAMINING TRENDS AND ITS APPLICATION TO REAL-WORLD ENVIRONMENT.pptx

Trends in datamining

1. TRENDS IN DATA MINING The field of data mining is dynamic, with ongoing improvements constantly emerging.

2. COLLECTIVE DATA MINING Data mining is typically focused on a single database or data warehouse. Still, in cases of data stored in multiple locations, Collective Data Mining (Distributed Data Mining) is effective in reaching and combining different databases. Federated Learning Blockchain-based Data Mining Distributed Data Warehousing

3. SEQUENCE AND TIME SERIES DATA MINING Sequence Data Mining (Time Series Data Mining) is commonly used for extracting patterns and trends from sequentially ordered data, enabling companies to track and estimate demand patterns for enhanced customer satisfaction. Predictive Maintenance in Manufacturing Financial Market Trend Analysis Healthcare Patient Monitoring

4. PHENOMENAL DATA MINING Phenomenal Data Mining focuses on the relationship between phenomena and data, using sources like invoices to gather demographic and purchasing habit information, but efficiently utilizing this trend may pose coding challenges. Customer Segmentation for Marketing Fraud Detection in Financial Transactions Supply Chain Optimization

5. UBIQUITOUS DATA MINING Mining data from mobile devices is a promising yet complex method, facing challenges such as privacy concerns, complexity in mobile data management, and the need for advanced algorithms to address these issues. Location-Based Services and Targeted Advertising Healthcare Monitoring and Analysis Traffic and Transportation Optimization

6. MULTIMEDIA DATA MINING This approach involves text mining and hypermedia mining, analyzing diverse data types like images, audio, video, and animation, using methods such as similarity research, clustering, association, and classification. Image Recognition and Classification Speech and Audio Analysis for Sentiment Analysis Video Surveillance and Anomaly Detection

7. SPATIAL AND GEOGRAPHIC DATA MINING Spatial and Geographic Data Mining combines technology with nature, extracting information about natural resources, satellites, and astronomical data, utilizing unique spatial data with distance and topological information. Applications include remote sensing, navigation, and medical imaging. Remote Sensing for Environmental Monitoring Location-Based Services and Geospatial Analytics Medical Imaging and Healthcare Planning

8. REFERENCES Ali, A. A., Varacha , P., Krayem , S., Zacek , P., & Urbanek , A. (2018). Distributed data mining systems: Techniques, approaches and algorithms. MATEC Web of Conferences , 210 , 1–6. https://doi.org/10.1051/matecconf/201821004038 Bashir, F., & Khokhar, A. (2000). Multimedia Systems : Content-Based Indexing and Retrieval. Image (Rochester, N.Y.) . Kim, C. (2009). Spatial Data Mining, Geovisualization . International Encyclopedia of Human Geography , 332–336. https://doi.org/10.1016/B978-008044910-4.00526-5 Lyons, D., & Tseytin , G. S. (1998). Phenomenal Data Mining and Link Analysis. AAAI Fall Symposium - Technical Report , FS - 98 - 01 , 68–75.,