Dissertation Topics On Data Mining: List Of Unique Ideas

When you want to do a dissertation on data mining, sometimes you draw a blank. It is not like you going into a store and asking the shop assistant and them being able to give you helpful hints.

Data mining research topics are plentiful. However, you need to consider different factors.

  1. Is it that you want to design or to improve the techniques of data mining or use the techniques for doing both designing as well as improving. You can contribute to society better if you work on improving the techniques rather than by just applying the techniques which already exist. This, however, will require both mathematical skills as well as better algorithmic skills of the researcher.

  2. Then you need to consider what kind of techniques are to be used or designed or even improved. You need to be specific and not be vague or write in general.

  3. You need to know what are the issues that you want to solve or what is it that you are aiming to improve and then you can narrow it down to how you intend going about it and the result from it all.

Once you can answer these questions then the topics you can choose from can be:

  1. How to detect the outliers in the stock market data imbalance

  2. How to optimize the efficiency of memory for algorithms dealing with subgraph mining – this will be in the context of community detection for social networks.

  3. Global development and Data Mining

  4. Public Administration and data analytics

  5. Practical value of data mining

  6. The framework of data mining

  7. Methods of data mining for large volumes of data

  8. Algorithms which are effective for large graphs

  9. Mining of text based on latent topics

  10. Semantic analysis of research papers

  11. Analysis of eco driving

  12. Analyzing disaster evacuation

  13. Analysis of congestion of traffic

  14. Similarity searches

  15. Intrinsic dimensionality

  16. Detection of outliers and dimensionality of data

  17. Feature selection when unsupervised

  18. Classification as well as applications of KNN

  19. Data clustering of distributed data

  20. Techniques to design intelligent interfaces which can be used for database systems for users without knowledge of SQL or tables.

  21. Modes of operations for different classes of networks.

As seen, the topics can be as varied as you can imagine, however, you need to choose one that interests you, and one that your instructor feels will be relevant. Try this company for help on any of the mentioned topics.

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