2018-2019 IEEE Projects on Data Mining


 
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2018-2019 IEEE Projects for CSE Based on Data Mining


Mining User-Aware Rare Sequential Topic Patterns in Document Streams
Abstract—Textual documents created and distributed on the Internet are ever changing in various forms. Most of existing works are devoted to topic modeling and the evolution of individual topics, while sequential relations of topics in successive documents published by a specific user are ignored. In this paper, in order to characterize and detect personalized and abnormal behaviors of Internet users, we propose Sequential Topic Patterns (STPs) and formulate the problem of mining User-aware Rare Sequential Topic Patterns (URSTPs) in document streams on the Internet. They are rare on the whole but relatively frequent for specific users, so can be applied in many real-life scenarios, such as real-time monitoring on abnormal user behaviors. We present a group of algorithms to solve this innovative mining problem through three phases: preprocessing to extract probabilistic topics and identify sessions for different users, generating all the STP candidates with (expected) support values for each user by pattern-growth, and selecting URSTPs by making user-aware rarity analysis on derived STPs. Experiments on both real (Twitter) and synthetic datasets show that our approach can indeed discover special users and interpretable URSTPs effectively and efficiently, which significantly reflect users’ characteristics.

2018-2019 Data Mining IEEE Projects


DATA Mining-Probabilistic Graph Databases
Resource Description Framework (RDF) has been widely used in the Semantic Web to describe resources and their relationships. The RDF graph is one of the most commonly used representations for RDF data. However, in many real applications such as the data extraction/integration, RDF graphs integrated from different data sources may often contain uncertain and inconsistent information (e.g., uncertain labels or that violate facts/rules), due to the unreliability of data sources. In this paper, we formalize the RDF data by inconsistent probabilistic RDF graphs, which contain both inconsistencies and uncertainty. With such a probabilistic graph model, we focus on an important problem, quality-aware subgraph matching over inconsistent probabilistic RDF graphs (QA-gMatch), which retrieves subgraphs from inconsistent probabilistic RDF graphs that are isomorphic to a given query graph and with high quality scores (considering both consistency and uncertainty). In order to efficiently answer QA-gMatch queries, we provide two effective pruning methods, namely adaptive label pruning and quality score pruning, which can greatly filter out false alarms of subgraphs. We also design an effective index to facilitate our proposed pruning methods, and propose an efficient approach for processing QA-gMatch

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2018-2019 IEEE Projects for CSE in Data Mining


1. A Comprehensive Study on Willingness Maximization for Social Activity Planning with Quality Guarantee

2. Aspect-level Influence Discovery from Graphs

3. Automatic Generation of Social Event Storyboard from Image Click-through Data

4. Connecting Social Media to E-Commerce: Cold-Start Product Recommendation using Microblogging Information

5. Crawling Hidden Objects with kNN Queries

6. Cross-Platform Identification of Anonymous Identical Users in Multiple Social Media Networks

7. Cyberbullying Detection based on Semantic-Enhanced Marginalized Denoising Auto-Encoder

8. DeyPoS: Deduplicatable Dynamic Proof of Storage for Multi-User Environments

9. D-ToSS: A Distributed Throwaway Spatial Index Structure for Dynamic Location Data

10. Efficiently Estimating Statistics of Points of Interests on Maps

11. Electronic Commerce Meets the Semantic Web

12. Encrypted Data Management with Deduplication in Cloud Computing

13. From Latency through Outbreak to Decline: Detecting Different States of Emergency Events Using Web Resources

14. Inverted Linear Quadtree: Efficient Top K Spatial Keyword Search

15. Mining High Utility Patterns in One Phase without Generating Candidates

16. Mining User-Aware Rare Sequential Topic Patterns in Document Streams

17. NATERGM: A Model for Examining the Role of Nodal Attributes in Dynamic Social Media Networks

18. Nearest Keyword Set Search in Multi-dimensional Datasets

19. Optimal Coding and Allocation for Perfect Secrecy in Multiple Clouds

20. Practical Approximate k Nearest Neighbor Queries with Location and Query Privacy

21. Prefix-adaptive and Time-sensitive Personalized Query Auto Completion

22. Quality-Aware Subgraph Matching Over Inconsistent Probabilistic Graph Databases

23. Relevance Feedback Algorithms Inspired By Quantum Detection

24. Resolving Multi-party Privacy Conflicts in Social Media

25. Sentiment Embeddings with Applications to Sentiment Analysis

26. Topic-Oriented Exploratory Search Based on an Indexing Network

27. Understanding Deep Representations Learned in Modeling Users Likes

2018-2019 IEEE Projects Based on Data Mining


Knowledge and Data Engineering
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

2018-2019 IEEE Projects for CSE in Data Mining

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

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

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

2018-2019 IEEE Projects in Data Mining


2018 IEEE Data Mining Projects
1.2DCrypt: Image Scaling and Cropping in Encrypted Domains

2. A Shoulder Surfing Resistant Graphical Authentication System

3. An Efficient Protocol with Bidirectional Verification for Storage Security in Cloud Computing

4. Conjunctive Keyword Search with Designated Tester and Timing Enabled Proxy Re-encryption

5. Dual-Server Public-Key Encryption with Keyword Search for Secure Cloud Storage

6. Efficient and Expressive Keyword Search Over Encrypted Data in Cloud

7. Enabling Personalized Search over Encrypted Outsourced Data with Efficiency Improvement

8. ID2S Password-Authenticated Key Exchange Protocols

9. Mitigating Cross-Site Scripting Attacks with a Content Security Policy

10. Online Subgraph Skyline Analysis Over Knowledge Graphs

11. Tell Me What You Eat, and I Will Tell You Where You Come From: A Data Science Approach for Global Recipe Data on the Web

12. Toward Efficient Multi-Keyword Fuzzy Search Over Encrypted Outsourced Data with Accuracy Improvement

13. TTSA: An Effective Scheduling Approach for Delay Bounded Tasks in Hybrid Clouds

14. Where You Are Is Who You Are User Identification by Matching Statistics



big data projects for btech students

IEEE Data Mining 10 Algorithms

2018-2019 IEEE Projects Based on Data Mining


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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2018-2019 Mini Projects on Data Mining


Keyword-based search in text-rich multi-dimensional datasets facilitates many novel applications and tools. In this paper, we consider objects that are tagged with keywords and are embedded in a vector space. For these datasets, we study queries that ask for the tightest groups of points satisfying a given set of keywords. We propose a novel method called ProMiSH (Projection and Multi Scale Hashing) that uses random projection and hash-based index structures, and achieves high scalability and speedup. We present an exact and an approximate version of the algorithm. Our experimental results on real and synthetic datasets show that ProMiSH has up to 60 times of speedup over state-of-the-art tree-based techniques