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2018-2019 Data Mining IEEE Projects

IEEE Data Mining Projects-2019 Titles

1.Achieving Data Truthfulness and Privacy Preservation in Data Markets

2.Application of Text Classification and Clustering of Twitter Data for Business Analytics

3.Frequent Itemsets Mining With Differential Privacy Over Large-Scale Data

4.Research on Kano Model Based on Online Comment Data Mining

5.Scalable Content-Aware Collaborative Filtering for Location Recommendation

ieee papers on Data Mining projects

6.Text Mining Based on Tax Comments as Big Data Analysis Using SVM and Feature Selection

7. Scalable Dynamic Graph Summarization

8. Conversion Prediction from Clickstream: Modeling Market Prediction and Customer Predictability

9. Design and Implementation of SSD-assisted Backup and Recovery for Database Systems

10. Learning to Weight for Text Classification

IEEE Projects in Data Mining

11. An Efficient Destination Prediction Approach Based on Future Trajectory Prediction and Transition Matrix Optimization

12. Untangling Blockchain: A Data Processing View of Blockchain Systems

Data Mining IEEE projects 2018

13. Knowledge Graph Embedding: A Survey of Approaches and Applications

14.Assembling and Using a Cellular Dataset for Mobile Network Analysis and Planning

15.Learning from Imbalanced Data

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Destination prediction is an essential task in various mobile applications. However, existing methods usually suffer from the problems of heavy computational burden, data sparsity and low coverage. Therefore a novel approach named DestPD is proposed to tackle these problems. It comprises two phases, the offline training and the online prediction. During the offline training, transition prababilities between two locations are obtained via Markov transition matrix multiplication. In order to improve the efficiency of matrix multiplication, we propose two data constructs, Efficient Transition Probability (ETP) and Transition Probabilities with Detours (TPD). They are capable of pinpointing the minimum amount of needed computation.

IEEE Data Mining projects 2018

What is data mining?
Data Mining Concepts and Techniques
Destination Prediction Approach Based on Future Trajectory
During the online prediction, we design Obligatory Update Point (OUP) and Transition Affected Area (TAA) to accelerate the frequent update of ETP and TPD for recomputing the transition probabilities. Moreover, a new future trajectory prediction approach is devised. Finally the destination is predicted by combining transition probabilities and the most probable future location through Bayesian reasoning. The DestPD method is proved to achieve one order of cut in both time and space complexity. Furthermore, the experimental results on real-world and synthetic datasets have shown that DestPD consistently surpasses the state-of-the-art methods in terms of both efficiency and accuracy


2018-2019 IEEE Projects on Data Mining Contact: 9591912372

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