A new density-based clustering approach in graph theoretic context

Date

2010-12-01

Author

İNKAYA, TÜLİN
Kayaligil, Sinan
Özdemirel, Nur Evin

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We consider the clustering problem with arbitrary shapes and different densities both within and between the clusters, where the number of clusters is unknown. We propose a new density-based approach in the graph theory context. The proposed algorithm has three phases. The first phase makes use of graph-based and density-based clustering approaches in order to identify the neighborhood structure of data points. The second phase detects outliers using the local outlier concept. In the third phase, a hiearchical agglomeration is performed to form the final clusters. The algorithm is tested on a number data sets and found to be effective. © 2010 IADIS.

Subject Keywords

Arbitrary shapes, Clustering, Density, Graph, Outlier

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=79955396615&origin=inward
https://hdl.handle.net/11511/107449

Conference Name

IADIS Int. Conf. Intelligent Systems and Agents 2010,ISA, IADIS European Conference on Data Mining 2010,DM, Part of the MCCSIS 2010

Collections

Department of Industrial Engineering, Conference / Seminar

Citation Formats

T. İNKAYA, S. Kayaligil, and N. E. Özdemirel, “A new density-based clustering approach in graph theoretic context,” presented at the IADIS Int. Conf. Intelligent Systems and Agents 2010,ISA, IADIS European Conference on Data Mining 2010,DM, Part of the MCCSIS 2010, Freiburg, Almanya, 2010, Accessed: 00, 2023. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=79955396615&origin=inward.