乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,6,26]],"date-time":"2022-06-26T09:29:14Z","timestamp":1656235754892},"reference-count":24,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,2,13]],"date-time":"2020-02-13T00:00:00Z","timestamp":1581552000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2020,2,13]],"date-time":"2020-02-13T00:00:00Z","timestamp":1581552000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Algorithms Mol Biol"],"published-print":{"date-parts":[[2020,12]]},"abstract":"Abstract<\/jats:title>\nBackground<\/jats:title>\nAdvances in molecular biology have resulted in big and complicated data sets, therefore a clustering approach that able to capture the actual structure and the hidden patterns of the data is required. Moreover, the geometric space may not reflects the actual similarity between the different objects. As a result, in this research we use clustering-based space that convert the geometric space of the molecular to a categorical space based on clustering results. Then we use this space for developing a new classification algorithm.<\/jats:p>\n<\/jats:sec>\nResults<\/jats:title>\nIn this study, we propose a new classification method named GrpClassifierEC<\/jats:italic> that replaces the given data space with categorical space based on ensemble clustering (EC). The EC space is defined by tracking the membership of the points over multiple runs of clustering algorithms. Different points that were included in the same clusters will be represented as a single point. Our algorithm classifies all these points as a single class. The similarity between two objects is defined as the number of times that these objects were not belong to the same cluster. In order to evaluate our suggested method, we compare its results to the k<\/jats:italic> nearest neighbors, Decision tree and Random forest classification algorithms on several benchmark datasets. The results confirm that the suggested new algorithm GrpClassifierEC<\/jats:italic> outperforms the other algorithms.<\/jats:p>\n<\/jats:sec>\nConclusions<\/jats:title>\nOur algorithm can be integrated with many other algorithms. In this research, we use only the k-means clustering algorithm with different k values. In future research, we propose several directions: (1) checking the effect of the clustering algorithm to build an ensemble clustering space. (2) Finding poor clustering results based on the training data, (3) reducing the volume of the data by combining similar points based on the EC.<\/jats:p>\n<\/jats:sec>\nAvailability and implementation<\/jats:title>\nThe KNIME workflow, implementing GrpClassifierEC<\/jats:italic>, is available at https:\/\/malikyousef.com<\/jats:ext-link><\/jats:p>\n<\/jats:sec>","DOI":"10.1186\/s13015-020-0162-7","type":"journal-article","created":{"date-parts":[[2020,2,13]],"date-time":"2020-02-13T07:03:47Z","timestamp":1581577427000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["GrpClassifierEC: a novel classification approach based on the ensemble clustering space"],"prefix":"10.1186","volume":"15","author":[{"given":"Loai","family":"Abdallah","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0001-8780-6303","authenticated-orcid":false,"given":"Malik","family":"Yousef","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,2,13]]},"reference":[{"key":"162_CR1","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1385\/MB:31:1:055","volume":"31","author":"Y Zhao","year":"2005","unstructured":"Zhao Y, Karypis G. 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