Enhanced Dark Block Extraction Method Performed Automatically to Determine the Number of Clusters in Unlabeled Data Sets
|
| Title | Enhanced Dark Block Extraction Method Performed Automatically to Determine the Number of Clusters in Unlabeled Data Sets |
| Authors | |
| Abstract | One of the major issues in data cluster analysis is to decide the numberof clusters or groups from a set of unlabeled data. In addition, the presentation ofcluster should be analyzed to provide the accuracy of clustering objects. This paperpropose a new method called Enhanced-Dark Block Extraction (E-DBE), which automaticallyidentifies the number of objects groups in unlabeled datasets. The proposedalgorithm relies on the available algorithm for visual assessment of cluster tendencyof a dataset, by using several common signal and image processing techniques. Themethod includes the following steps: 1.Generating an Enhanced Visual AssessmentTendency (E-VAT) image from a dissimilarity matrix which is the input for E-DBEalgorithm. 2. Processing image segmentation on E-VAT image to obtain a binaryimage then performs filter techniques. 3. Performing distance transformation to thefiltered binary image and projecting the pixels in the main diagonal alignment ofthe image to figure a projection signal. 4. Smoothing the outcrop signal, computingits first-order derivative and then detecting major peaks and valleys in the resultingsignal to acquire the number of clusters. E-DBE is a parameter-free algorithm toperform cluster analysis. Experiments of the method are presented on several UCI,synthetic and real world datasets. |
| Publisher | Agora University of Oradea |
| Date | 2013-02-18 |
| Source | International Journal of Computers Communications & Control Vol 8, No 2 (2013): INT J COMPUT COMMUN |
| Rights | Copyright Transfer FormThe undersigned Author(s) of the above mentioned Paper here by transfer any and all copyright-rights in and to The Paper to The Publisher.The Author(s) warrants that The Paper is based on their original work and that the undersigned has the power and authority to make and execute this assignment. It is the author´s responsibility to obtain written permission to quote material that has been previously published in any form.The Publisher recognizes the retained rights noted below and grants to the above authors and employers for whom the work performed royalty-free permission to reuse their materials below. Authors may reuse all or portions of the above Paper in other works, excepting the publication of the paper in the same form. Authors may reproduce or authorize others to reproduce the above Paper for the Author´s personal use or for internal company use, provided that the source and The Publisher copyright notice are mentioned, that the copies are not used in any way that implies The Publisher endorsement of a product or service of an employer, and that the copies are not offered for sale as such. Authors are permitted to grant third party requests for reprinting, republishing or other types of reuse. The Authors may make limited distribution of all or portions of the above Paper prior to publication if they inform The Publisher of the nature and extent of such limited distribution prior there to. Authors retain all proprietary rights in any process, procedure, or article of manufacture described in The Paper. This agreement becomes null and void if and only if the above paper is not accepted and published by The Publisher, or is withdrawn by the author(s) before acceptance by the Publisher. |