Thesis Data Mining

Please suggest some topics or project that would make for a good masters thesis subject. Many good questions generate some degree of opinion based on expert experience, but answers to this question will tend to be almost entirely based on opinions, rather than facts, references, or specific expertise.I think this has advantages because these papers outline details regarding data as well -- perhaps you can use the same.Data mining is all about extracting necessary or required information with the aid of various methods from a mass of data.

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We apply this algorithm to predict combinatorial regulation of transcription factors.

We also extend the algorithm to generate 3-clusters in order to capture associations between different classes of entities.

I have found a very interesting subject: "Predicting customer churn using decision tree" or either "Predicting employee turnover using decision tree", I looked around very hard but unfortunately couldn't find any relevant dataset to download (Telecommunication Customer churn Dataset ).

I would like to work on a similar subject using "Decision Tree Technique".

For more information about writing a master's theses in our group, please see here.

In the Web Science Group, we are particularly interested in Text Mining to deal with the vast amount of unstructured and semi-structured data (on the Web). Felix Naumann Information Systems E-Mail: felix.naumann(at)Assistant: Diana Stephan Office: Campus II, House F, F-2.01 Tel.: 49 (0)331 5509-280 Fax: 49 (0)331 5509-287 E-Mail: office-naumann(at)To visit us, please see these directions.

If you are too, maybe some of the following topics spark your interest.

I am looking for a thesis to complete my master, I am interested in Predictive Analytics in marketing, HR, management or financial subject, using Data Mining Application.

In this dissertation, we present our work to extract hidden knowledge from data about the large-scale complex biological systems that usually involve heterogeneous entities and associations between them.

First, we propose a biclustering algorithm to identify entities that may manifest cohesiveness within a subspace of conditions.