Data Mining Education Research Topics

Data mining for education research topics are widely used in many domains and it is now one of the most essential topics. It is widely used in many regions and applications. Then the following are the concepts, information and explanations for data mining related research ideas:

  1. Define Data Mining

Initially we start with the definition of Data Mining. It is the process of taking out important information, understandings from large dataset and patterns. It consists of utilizing different methods and algorithms to find invisible trends, knowledge and relationships over the data that can be helpful for creating instructed choices and forecasting in different domains like healthcare, business and science.

  1. What is Data Mining?

Then at the next stage we look for a deep interpretation for Data Mining.  It is the process of examining and seeking a large amount of raw data for the purpose of finding designs and taking out valuable information. To know more about the consumers, companies utilize this Data mining software. It will assist them to create more efficient market policies in lower costs and enhance sales.

  1. Where Data Mining used?

After the deep interpretation we understand where to employ Data mining techniques. It is used in an extensive range of domains and industries to find invisible understandings, patterns and relationships over big datasets. Some general applications and regions where data mining is employed will contain Scientific Research, Business and Finance, Education, Environmental science, Healthcare, Manufacturing and Quality control and Telecommunication.

  1. Why Data Mining technology proposed? , previous technology issues

The data mining technology is proposed to assist associations by taking out the precious information and patterns from big datasets. It permits institutions, business and researchers to create instructed choices, detect invisible relationships and find trends that will not be clear over traditional data analysis techniques. This technology is a strong tool for optimizing processes, enhancing decision-making, and obtaining an inexpensive edge in different domains by manipulating the resource of details included in data. The data mining technology handles some problems and difficulties in the existing technologies are Interpretability, Privacy Concerns, Data Quality, Scalability and Complexity.

  1. Algorithms / Protocols

Now we see the algorithms or methods to be used for this proposed research. Our proposed data mining technology uses methods like GaussianFlow Optimizer (GFO), Genetic Algorithm – optimized Latent Dirichlet Allocation (GA-LDA) and Hybrid Logistic Regression- K-Nearest Neighbor.

  1. Comparative study / Analysis

We propose a data mining technology and this overcomes some previous technology issues to overcome that. The methods that we compared for analysis are:

  • To improve the feature extraction, enabling the creation of complex patterns connected to the student’s hazard.
  • Gaussian Flow Optimizer (GFO) is used to understand model decisions to separate important characteristics, implementing canopy clustering with our proposed GFO for exact student assembling.
  • For forecasting the hazard-students we utilize Logistic Regression with K-Nearest Neighbor this combined technique helps to enhance the exact accuracy by using both of these factors.
  1. Simulation results / parameters

In this research we proposed a novel technique and those techniques are compared with some of the performance metrics or parameters to obtain the best findings for our research. The metrics that we compared are Specificity, F1-score, Accuracy, Recall, Sensitivity and Precision.

  1. Dataset LINKS / Important URL

Here we propose the data mining technique; this technique addresses some possible issues. The following are the links that are useful when we go through the descriptions of our proposed technique.

  1. Data Mining Applications

There are several applications along different domains and industries for the data mining technology. Few of the general data mining applications like Healthcare Analytics, Education Analysis, Environmental Analysis, Fraud Detection and Human Resource and Talent Management.

  1. Topology for Data Mining

For data mining, the term “topology” generally defines the organization or framework of relationships, design or data over a system or dataset, instead of network topologies. Some general kinds of data structures or data mining topologies are Spatial Topology, Hierarchical Topology, Network Topology, Feature space topology, Grid-based Topology, Textual Topology, Sequential topology and Time series topology.

  1. Environment in Data Mining

Let’s look over the topology to be used for data mining technology. It generally contains a grouping of human expertise, software tools and hardware infrastructure. Its environment frequently contains some powerful servers or computers that have the ability to process huge amounts of datasets, programming languages and expert data mining software, data storage answers or knowledgeable data scientists or analysts who can develop, understand and execute data mining techniques and methods. It also contains data integration, model estimation and data preprocessing elements to make sure the efficacy and quality of our data mining process. Moreover the environment in data mining frequently needs accessibility to similar databases for examining and data sources.

  1. Simulation Tools

Data mining technology is proposed in this research and is implemented by utilizing the following simulation tool or the software requirements. The tool here we used for this research is Python 3.11.3 and the programming language that we use is Python. Then the processor here employed is 2.5 GHz and above. The operating system that used to execute the work is windows 10 (64 – bit).

  1. Results

Data mining technology is proposed in this research and this will come across several issues in the existing technology. In this we compare our proposed technology performance metrics with the previous methods to get the best outcome. This is executed by using the tool Python 3.11.3.

Data Mining Education Research Ideas:

The following are the research topics that are based on our proposed data mining based applications, techniques or methods and some other details of our proposed research, these are useful when we overview the concepts.

  1. Process mining and data mining applications in the domain of chronic diseases: A systematic review
  2. Design and realization of data mining simulation and methodological models
  3. An unsupervised data mining-based framework for evaluation and optimization of operation strategy of HVAC system
  4. A comparative study on data mining models for weather forecasting: A case study on chittagong, Bangladesh
  5. Enhancing urban flood forecasting in drainage systems using dynamic ensemble-based data mining
  6. Classification Technique and its Combination with Clustering and Association Rule Mining in Educational Data Mining — A survey
  7. An Analysis of Data Mining Techniques in Software Engineering Database Design
  8. Data mining-guided alleviation of hyperuricemia by Paeonia veitchii Lynch through inhibition of xanthine oxidase and regulation of renal urate transporters
  9. Correlation knowledge extraction based on data mining for distribution network planning
  10. Research on the application of data mining technology in software engineering
  11. Integrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective
  12. A Systematic Review of Social Media Data Mining on Android
  13. Using data mining techniques deep analysis and theoretical investigation of COVID-19 pandemic
  14. Systematic analysis on school violence and bullying using data mining
  15. Flood prediction with time series data mining: Systematic review
  16. A Survey of Tax Risk Detection Using Data Mining Techniques
  17. Multi-attribute decision-making based on data mining under a dynamic hybrid trust network
  18. A systematic review of data mining applications in kidney transplantation
  19. Data Mining for the Global Multiplex Weekly Average Income Analysis
  20. Design and Application of Human resource management system Based on Data Mining Technology
  21. A data mining transmission switching heuristic for post-contingency AC power flow violation reduction in real-world, large-scale systems
  22. Data mining approach for dry bean seeds classification
  23. Application of data mining combined with power data in assessment and prevention of regional atmospheric pollution
  24. E-learning enhancement through educational data mining with Covid-19 outbreak period in backdrop: A review
  25. Optimal location selection for a distributed hybrid renewable energy system in rural Western Australia: A data mining approach
  26. OCPMDM 2.0: An intelligent solution for materials data mining
  27. Data mining of social media for urban resilience study: A case of rainstorm in Xi’an
  28. A DT-CWT and Data mining based approach for High Impedance Fault Diagnosis in Micro-grid System
  29. Smart Trip Prediction Model for Metro Traffic Control Using Data Mining Techniques
  30. Sports Training Strategies Based on Data Mining Technology
  31. The L2 convergence of stream data mining algorithms based on probabilistic neural networks
  32. Sequential data mining of infection patterns as predictors for onset of type 1 diabetes in genetically at-risk individuals
  33. Application of Data Mining Technology in Exam Score Analysis
  34. Optimal design of a supercritical carbon dioxide recompression cycle using deep neural network and data mining techniques
  35. Integrated data mining for prediction of specific capacitance of porous carbon materials for flexible energy storage devices
  36. Dynamics of pesticides in surface water bodies by applying data mining to spatiotemporal big data. A case study for the Puglia Region
  37. Accelerated alloy discovery using synthetic data generation and data mining
  38. Data Mining applied to Knowledge Management
  39. Machine learning and data mining methodology to predict nominal and numeric performance body weight values using Large White male turkey datasets
  40. A new data mining strategy for performance evaluation of a shared energy recovery system integrated with data centres and district heating networks
  41. Using data mining technology to explore causes of inaccurate reliability data and suggestions for maintenance management
  42. Investigating the association of acute kidney injury (AKI) with COVID-19 mortality using data-mining scheme
  43. Academic data derived from a university e-government analytic platform: An educational data mining approach
  44. Understanding children’s cycling route selection through spatial trajectory data mining
  45. Identifying underlying influential factors in information diffusion process on social media platform: A hybrid approach of data mining and time series regression
  46. Kendrick mass defect analysis-based data mining technique for trace components in polyolefins observed by pyrolysis-gas chromatography/high-resolution mass spectrometry
  47. A data mining framework for reporting trends in the predictive contribution of factors related to educational achievement
  48. Intelligent ship inspection analytics: Ship deficiency data mining for port state control
  49. Resurrecting the market factor: A case of data mining across international markets
  50. Effect of Siegesbeckiae Herba on immune-inflammation of rheumatoid arthritis: Data mining and network pharmacology