loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Empirical Evaluation of Distance Measures for Nearest Point with Indexing Ratio Clustering Algorithm

Topics: 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

Authors: Raneem Qaddoura 1 ; Hossam Faris 2 ; Ibrahim Aljarah 2 ; J. Merelo 3 and Pedro Castillo 3

Affiliations: 1 Information Technology, Philadelphia University, Amman, Jordan ; 2 Department of Business Information Technology, King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan ; 3 ETSIIT-CITIC, University of Granada, Granada, Spain

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)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.238.199.4

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
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 - NCTA, ISBN 978-989-758-475-6; ISSN 2184-2825, pages 430-438. DOI: 10.5220/0010121504300438

@conference{ncta20,
author={Raneem Qaddoura. and Hossam Faris. and Ibrahim Aljarah. and J. Merelo. and Pedro 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 - NCTA,},
year={2020},
pages={430-438},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010121504300438},
isbn={978-989-758-475-6},
issn={2184-2825},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computational Intelligence - NCTA,
TI - Empirical Evaluation of Distance Measures for Nearest Point with Indexing Ratio Clustering Algorithm
SN - 978-989-758-475-6
IS - 2184-2825
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