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Empirical Evaluation of Distance Measures for Nearest Point with Indexing Ratio Clustering AlgorithmTopics: Applications: Image Processing and Artificial Vision, Pattern Recognition, Decision Making, Industrial and Real World Applications, Financial Applications, Neural Prostheses and Medical Applications, Neural Based Data Mining and Complex Information Process; Self-Organizing Maps (SOM) and Self-organizing Systems

Keyword(s):Clustering, Cluster Analysis, Distance Measure, Nearest Point with Indexing Ratio, NPIR, Nearest Point, Indexing Ratio, Nearest Neighbor Search Technique.

Abstract: Selecting the proper distance measure is very challenging for most clustering algorithms. Some common distance measures include Manhattan (City-block), Euclidean, Minkowski, and Chebyshev. The so called Nearest Point with Indexing Ratio (NPIR) is a recent clustering algorithm, which tries to overcome the limitations of other algorithms by identifying arbitrary shapes of clusters, non-spherical distribution of points, and shapes with different densities. It does so by iteratively utilizing the nearest neighbors search technique to find different clusters. The current implementation of the algorithm considers the Euclidean distance measure, which is used for the experiments presented in the original paper of the algorithm. In this paper, the impact of the four common distance measures on NPIR clustering algorithm is investigated. The performance of NPIR algorithm in accordance to purity and entropy measures is investigated on nine data sets. The comparative study demonstrates that the NPIR generates better results when Manhattan distance measure is used compared to the other distance measures for the studied high dimensional data sets in terms of purity and entropy.(More)

Selecting the proper distance measure is very challenging for most clustering algorithms. Some common distance measures include Manhattan (City-block), Euclidean, Minkowski, and Chebyshev. The so called Nearest Point with Indexing Ratio (NPIR) is a recent clustering algorithm, which tries to overcome the limitations of other algorithms by identifying arbitrary shapes of clusters, non-spherical distribution of points, and shapes with different densities. It does so by iteratively utilizing the nearest neighbors search technique to find different clusters. The current implementation of the algorithm considers the Euclidean distance measure, which is used for the experiments presented in the original paper of the algorithm. In this paper, the impact of the four common distance measures on NPIR clustering algorithm is investigated. The performance of NPIR algorithm in accordance to purity and entropy measures is investigated on nine data sets. The comparative study demonstrates that the NPIR generates better results when Manhattan distance measure is used compared to the other distance measures for the studied high dimensional data sets in terms of purity and entropy.

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Qaddoura, R.; Faris, H.; Aljarah, I.; Merelo, J. and Castillo, P. (2020). Empirical Evaluation of Distance Measures for Nearest Point with Indexing Ratio Clustering Algorithm. In Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - NCTA; ISBN 978-989-758-475-6; ISSN 2184-3236, SciTePress, pages 430-438. DOI: 10.5220/0010121504300438

@conference{ncta20, author={Raneem Qaddoura. and Hossam Faris. and Ibrahim Aljarah. and J. J. Merelo. and Pedro A. Castillo.}, title={Empirical Evaluation of Distance Measures for Nearest Point with Indexing Ratio Clustering Algorithm}, booktitle={Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - NCTA}, year={2020}, pages={430-438}, publisher={SciTePress}, organization={INSTICC}, doi={10.5220/0010121504300438}, isbn={978-989-758-475-6}, issn={2184-3236}, }

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - NCTA TI - Empirical Evaluation of Distance Measures for Nearest Point with Indexing Ratio Clustering Algorithm SN - 978-989-758-475-6 IS - 2184-3236 AU - Qaddoura, R. AU - Faris, H. AU - Aljarah, I. AU - Merelo, J. AU - Castillo, P. PY - 2020 SP - 430 EP - 438 DO - 10.5220/0010121504300438 PB - SciTePress

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