In Panigrahi, C. R., Pati, B., Mohapatra, P., Buyya, R., & Li, K., editors, *Progress in Advanced Computing and Intelligent Engineering*, of *Advances in Intelligent Systems and Computing*, pages 213–226, Singapore, 2021. Springer.

doi abstract bibtex

doi abstract bibtex

The objectives of this research are related to study the DBSCAN algorithm and engineer an enhancement to this algorithm addressing its flaws. DBSCAN is criticized for its requirement to input two parameters, namely—epsilon radius (ϵ) and minimum number of points (MinPts). It is difficult to know beforehand the optimum value of both parameters, and hence many trials are required until desired clusters are obtained. Also, in a dataset, a cluster’s density can vary. DBSCAN fails to identify clusters with density variations present. The proposed algorithm Adaptive Epsilon DBSCAN (AEDBSCAN), generates epsilon dynamically in accordance with the neighborhood of a point and thereafter adopts DBSCAN clustering with the corresponding epsilon to obtain the clusters. Experimental results are obtained from testing AEDBSCAN on artificial datasets. The experimental results confirm that the proposed AEDBSCAN algorithm efficiently carries out multi-density clustering than the original DBSCAN.

@inproceedings{mistry_aedbscanadaptive_2021, address = {Singapore}, series = {Advances in {Intelligent} {Systems} and {Computing}}, title = {{AEDBSCAN}—{Adaptive} {Epsilon} {Density}-{Based} {Spatial} {Clustering} of {Applications} with {Noise}}, isbn = {9789811563539}, doi = {10.1007/978-981-15-6353-9_20}, abstract = {The objectives of this research are related to study the DBSCAN algorithm and engineer an enhancement to this algorithm addressing its flaws. DBSCAN is criticized for its requirement to input two parameters, namely—epsilon radius (ϵ) and minimum number of points (MinPts). It is difficult to know beforehand the optimum value of both parameters, and hence many trials are required until desired clusters are obtained. Also, in a dataset, a cluster’s density can vary. DBSCAN fails to identify clusters with density variations present. The proposed algorithm Adaptive Epsilon DBSCAN (AEDBSCAN), generates epsilon dynamically in accordance with the neighborhood of a point and thereafter adopts DBSCAN clustering with the corresponding epsilon to obtain the clusters. Experimental results are obtained from testing AEDBSCAN on artificial datasets. The experimental results confirm that the proposed AEDBSCAN algorithm efficiently carries out multi-density clustering than the original DBSCAN.}, language = {en}, booktitle = {Progress in {Advanced} {Computing} and {Intelligent} {Engineering}}, publisher = {Springer}, author = {Mistry, Vidhi and Pandya, Urja and Rathwa, Anjana and Kachroo, Himani and Jivani, Anjali}, editor = {Panigrahi, Chhabi Rani and Pati, Bibudhendu and Mohapatra, Prasant and Buyya, Rajkumar and Li, Kuan-Ching}, year = {2021}, keywords = {Adaptive epsilon, DBSCAN, Data mining, Density-based clustering, Multi-density}, pages = {213--226}, }

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