Title | Clustering with k-nearest neighbor consistency |
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Description |
The objective is to add an efficient implementation of the following algorithm into the ELKI data mining framework (this requires good Java knowledge): Ding, C., & He, X. (2004, March). K-nearest-neighbor consistency in data clustering: incorporating local information into global optimization. In Proceedings of the 2004 ACM symposium on Applied computing (pp. 584-589). and study the result quality compared to existing algorithms in ELKI. It is required that the implementation follows the object-oriented design patterns of ELKI, i.e., reusability and modularity. This likely requires the method to be implemented in two parts: the knn-based evaluation measure as well as the k-means-based optimization algorithm. The algorithm must be tested with appropriate unit tests. |
Qualification |
This is only suitable as a Bachelor thesis topic. Good Java programming skills Good understanding of data mining algorithms Good statistical knowledge |
Thesistype | Bachelorthesis |
Second Tutor | Lang, Andreas |
Professor | Schubert, Erich |
Assigned To | Strahmann, Niklas |
Status | Bearbeitung |
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Registered On | Sep 26, 2022 3:27:00 PM |
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