{"title":"基于特征空间的模糊c均值核化印尼新闻主题检测","authors":"Mukti Ari, H. Murfi","doi":"10.1109/ICOICT.2018.8528786","DOIUrl":null,"url":null,"abstract":"Topic detection is practical methods to find a topic in a collection of documents. One of the methods is a clustering-based method whose centroids are interpreted as topics, i.e., eigenspace-based fuzzy c-means (EFCM). The clustering process of the EFCM method is performed in a smaller dimensional Eigenspace. Thus, the accuracy of the clustering process may be reduced. In this paper, we use the kernel method so that the clustering process is performed in a higher dimensional space without transforming data into that space. Our simulations show that this kernelization improves the accuracies of EFCM in term of interpretability scores for Indonesian news.","PeriodicalId":266335,"journal":{"name":"2018 6th International Conference on Information and Communication Technology (ICoICT)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Kernelization of Eigenspace-Based Fuzzy C-Means for Topic Detection on Indonesian News\",\"authors\":\"Mukti Ari, H. Murfi\",\"doi\":\"10.1109/ICOICT.2018.8528786\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Topic detection is practical methods to find a topic in a collection of documents. One of the methods is a clustering-based method whose centroids are interpreted as topics, i.e., eigenspace-based fuzzy c-means (EFCM). The clustering process of the EFCM method is performed in a smaller dimensional Eigenspace. Thus, the accuracy of the clustering process may be reduced. In this paper, we use the kernel method so that the clustering process is performed in a higher dimensional space without transforming data into that space. Our simulations show that this kernelization improves the accuracies of EFCM in term of interpretability scores for Indonesian news.\",\"PeriodicalId\":266335,\"journal\":{\"name\":\"2018 6th International Conference on Information and Communication Technology (ICoICT)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 6th International Conference on Information and Communication Technology (ICoICT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICOICT.2018.8528786\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 6th International Conference on Information and Communication Technology (ICoICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICOICT.2018.8528786","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Kernelization of Eigenspace-Based Fuzzy C-Means for Topic Detection on Indonesian News
Topic detection is practical methods to find a topic in a collection of documents. One of the methods is a clustering-based method whose centroids are interpreted as topics, i.e., eigenspace-based fuzzy c-means (EFCM). The clustering process of the EFCM method is performed in a smaller dimensional Eigenspace. Thus, the accuracy of the clustering process may be reduced. In this paper, we use the kernel method so that the clustering process is performed in a higher dimensional space without transforming data into that space. Our simulations show that this kernelization improves the accuracies of EFCM in term of interpretability scores for Indonesian news.