@inproceedings{
 title = {Comparative study of distance measures for the fuzzy C-means and K-means non-supervised methods applied to image segmentation},
 type = {inproceedings},
 year = {2020},
 identifiers = {[object Object]},
 keywords = {Clustering,Image segmentation,Non-supervised algorithms},
 websites = {http://ceur-ws.org/Vol-2714/icaiw_waai_1.pdf},
 id = {7ee20413-55d7-39f2-87ed-c57f6f74cd73},
 created = {2020-12-30T02:11:40.207Z},
 file_attached = {false},
 profile_id = {e5f1b339-ec56-313b-b123-fd0a1c527f0d},
 last_modified = {2020-12-30T02:11:40.207Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Velez-Falconi2020},
 source_type = {inproceedings},
 private_publication = {false},
 abstract = {Recent studies have revealed that the performance of the FCM and K-means is completely related to the distance measures. However, the literature does not provide evidence that the distance used for data-clustering is useful for image segmentation. Therefore, a comparative study of the performance of different distance measures applied to image segmentation, using the mentioned clustering methods is proposed in this work. The selection of the distance measures was based on a literature study of their benefits. As a consequence, the selected distances to be tested are Euclidean, Manhattan, Canberra, and Spearman. Since our principal goal is to compare the effectiveness of the distance, the experiment had been evaluated according to two centroids selected by the user. According to primary results, the best-rated distance employed for image segmentation is the Canberra distance.},
 bibtype = {inproceedings},
 author = {Vélez-Falconí, Martín and Marín, Josué and Jiménez, Selena and Guachi-Guachi, Lorena},
 booktitle = {CEUR Workshop Proceedings}
}

{"_id":"dDgMoQLFGRzv2Gi8K","bibbaseid":"vlezfalcon-marn-jimnez-guachiguachi-comparativestudyofdistancemeasuresforthefuzzycmeansandkmeansnonsupervisedmethodsappliedtoimagesegmentation-2020","author_short":["Vélez-Falconí, M.","Marín, J.","Jiménez, S.","Guachi-Guachi, L."],"bibdata":{"title":"Comparative study of distance measures for the fuzzy C-means and K-means non-supervised methods applied to image segmentation","type":"inproceedings","year":"2020","identifiers":"[object Object]","keywords":"Clustering,Image segmentation,Non-supervised algorithms","websites":"http://ceur-ws.org/Vol-2714/icaiw_waai_1.pdf","id":"7ee20413-55d7-39f2-87ed-c57f6f74cd73","created":"2020-12-30T02:11:40.207Z","file_attached":false,"profile_id":"e5f1b339-ec56-313b-b123-fd0a1c527f0d","last_modified":"2020-12-30T02:11:40.207Z","read":false,"starred":false,"authored":"true","confirmed":"true","hidden":false,"citation_key":"Velez-Falconi2020","source_type":"inproceedings","private_publication":false,"abstract":"Recent studies have revealed that the performance of the FCM and K-means is completely related to the distance measures. However, the literature does not provide evidence that the distance used for data-clustering is useful for image segmentation. Therefore, a comparative study of the performance of different distance measures applied to image segmentation, using the mentioned clustering methods is proposed in this work. The selection of the distance measures was based on a literature study of their benefits. As a consequence, the selected distances to be tested are Euclidean, Manhattan, Canberra, and Spearman. Since our principal goal is to compare the effectiveness of the distance, the experiment had been evaluated according to two centroids selected by the user. According to primary results, the best-rated distance employed for image segmentation is the Canberra distance.","bibtype":"inproceedings","author":"Vélez-Falconí, Martín and Marín, Josué and Jiménez, Selena and Guachi-Guachi, Lorena","booktitle":"CEUR Workshop Proceedings","bibtex":"@inproceedings{\n title = {Comparative study of distance measures for the fuzzy C-means and K-means non-supervised methods applied to image segmentation},\n type = {inproceedings},\n year = {2020},\n identifiers = {[object Object]},\n keywords = {Clustering,Image segmentation,Non-supervised algorithms},\n websites = {http://ceur-ws.org/Vol-2714/icaiw_waai_1.pdf},\n id = {7ee20413-55d7-39f2-87ed-c57f6f74cd73},\n created = {2020-12-30T02:11:40.207Z},\n file_attached = {false},\n profile_id = {e5f1b339-ec56-313b-b123-fd0a1c527f0d},\n last_modified = {2020-12-30T02:11:40.207Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Velez-Falconi2020},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {Recent studies have revealed that the performance of the FCM and K-means is completely related to the distance measures. However, the literature does not provide evidence that the distance used for data-clustering is useful for image segmentation. Therefore, a comparative study of the performance of different distance measures applied to image segmentation, using the mentioned clustering methods is proposed in this work. The selection of the distance measures was based on a literature study of their benefits. As a consequence, the selected distances to be tested are Euclidean, Manhattan, Canberra, and Spearman. Since our principal goal is to compare the effectiveness of the distance, the experiment had been evaluated according to two centroids selected by the user. According to primary results, the best-rated distance employed for image segmentation is the Canberra distance.},\n bibtype = {inproceedings},\n author = {Vélez-Falconí, Martín and Marín, Josué and Jiménez, Selena and Guachi-Guachi, Lorena},\n booktitle = {CEUR Workshop Proceedings}\n}","author_short":["Vélez-Falconí, M.","Marín, J.","Jiménez, S.","Guachi-Guachi, L."],"urls":{"Website":"http://ceur-ws.org/Vol-2714/icaiw_waai_1.pdf"},"biburl":"https://bibbase.org/service/mendeley/e5f1b339-ec56-313b-b123-fd0a1c527f0d","bibbaseid":"vlezfalcon-marn-jimnez-guachiguachi-comparativestudyofdistancemeasuresforthefuzzycmeansandkmeansnonsupervisedmethodsappliedtoimagesegmentation-2020","role":"author","keyword":["Clustering","Image segmentation","Non-supervised algorithms"],"metadata":{"authorlinks":{}}},"bibtype":"inproceedings","biburl":"https://bibbase.org/service/mendeley/e5f1b339-ec56-313b-b123-fd0a1c527f0d","dataSources":["GC3EkcEr2RMH3uBPn","ya2CyA73rpZseyrZ8","AFewjTf6FwipjxxXM"],"keywords":["clustering","image segmentation","non-supervised algorithms"],"search_terms":["comparative","study","distance","measures","fuzzy","means","means","non","supervised","methods","applied","image","segmentation","vélez-falconí","marín","jiménez","guachi-guachi"],"title":"Comparative study of distance measures for the fuzzy C-means and K-means non-supervised methods applied to image segmentation","year":2020}