Final Year Major Data Mining projects Guntur
BTECH MAJOR DATA Mining Projects
|1||A Hidden Markov Contour Tree Model for Spatial Structured Prediction||DATA MINING|
|2||Adaptive Diffusion of Sensitive Information In Online Social Networks||DATA MINING|
|3||Adaptive Similarity Embedding For Unsupervised Multi-View Feature Selection||DATA MINING|
|4||Adaptively Secure and Fast Processing of Conjunctive Queries over Encrypted Data||DATA MINING|
|5||BATCH A Scalable Asymmetric Discrete Cross-Modal Hashing||DATA MINING|
|6||Cashless Society Managing Privacy and Security in the Technological Age||DATA MINING|
|7||DGDFS Dependence Guided Discriminative Feature Selection for Predicting Adverse Drug-Drug Interaction||DATA MINING|
|8||Discovering Temporal Patterns for Event Sequence Clustering via Policy Mixture Model||DATA MINING|
|9||Effective Keyword Search over Weighted Graphs||DATA MINING|
|10||Efficient Match-Based Candidate Network Generation for Keyword Queries over Relational Databases||DATA MINING|
|11||Efficient Processing of Group Planning Queries Over Spatial-Social Networks||DATA MINING|
|12||Efficiently Processing Spatial and Keyword Queries in Indoor Venues||DATA MINING|
|13||Evaluating Public Anxiety for Topic-based Communities in Social Networks||DATA MINING|
|14||Fast Multi-view Semi-supervised Learning with Learned Graph||DATA MINING|
|15||Fuzzy K-Means Clustering With Discriminative Embedding||DATA MINING|
|16||Guarantee the Quality-of-Service of Control Transactions in Real-Time Database Systems||DATA MINING|
|17||Hierarchical Prediction and Adversarial Learning For Conditional Response Generation||DATA MINING|
|18||Land Use Classification with Point of Interests and Structural Patterns||DATA MINING|
|19||Modeling Relation Paths for Knowledge Graph Completion||DATA MINING|
|20||Multi-view Clustering with the Cooperation of Visible and Hidden Views||DATA MINING|
|21||Online Pricing with Reserve Price Constraint for Personal Data Markets||DATA MINING|
|22||Personalized Long and Short term Preference Learning for Next POI Recommendation||DATA MINING|
|23||Predicting Hot Events in the Early Period through Bayesian Model for Social Networks||DATA MINING|
|24||Predicting Student Performance in an Educational Game Using a Hidden Markov Model||DATA MINING|
|25||Privacy Preserving Location Data Publishing A Machine Learning Approach||DATA MINING|
|26||Product Quantized Collaborative Filtering||DATA MINING|
|27||Publicly Verifiable Databases with All Efficient Updating Operations||DATA MINING|
|28||Scalable and Adaptive Data Replica Placement for Geo-Distributed Cloud Storages||DATA MINING|
|29||Social Boosted Recommendation with Folded Bipartite Network Embedding||DATA MINING|
|30||Emotion Correlation Mining Through Deep Learning Models on Natural Language Text||DATA MINING|
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Cloud computing is a type of technology that uses the internet to provide clients with a variety of services. It gives you access to a wide range of technological resources, including storage, servers, and much more. It enables you to store data in huge quantities and in the quantities required so that you can recover data in the case of a system crash, server crash, or other unforeseen incident. Python has capabilities that can assist you in developing applications, so it can be utilized in SaaS, IaaS, and PaaS development. Whatever requirement we have, it is capable of helping us in our businesses by developing applications from remote locations or in helping different businesses to exchange data with others. Now that you have understood the applications of python, do not wait further, and choose from our b tech academic CSE mini python projects.
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