Density based clustering using mathematical morphology

Thesis Type: Postgraduate

Institution Of The Thesis: Middle East Technical University, Turkey

Approval Date: 2007

Thesis Language: English

Student: Coşku Erdem



Improvements in technology, enables us to store large amounts of data in warehouses. In parallel, the need for processing this vast amount of raw data and translating it into interpretable information also increases. A commonly used solution method for the described problem in data mining is clustering. We propose "Density Based Clustering Using Mathematical Morphology" (DBCM) algorithm as an effective clustering method for extracting arbitrary shaped clusters of noisy numerical data in a reasonable time. This algorithm is predicated on the analogy between images and data warehouses. It applies grayscale morphology which is an image processing technique on multidimensional data. In this study we evaluated the performance of the proposed algorithm on both synthetic and real data and observed that the algorithm produces successful and interpretable results with appropriate parameters. In addition, we computed the computational complexity to be linear on number of data points for low dimensional data and exponential on number of dimensions for high dimensional data mainly due to the morphology operations.