@article{zhang_data_2016,
	series = {Signal {Processing} for {Heterogeneous} {Sensor} {Networks}},
	title = {Data stream clustering based on {Fuzzy} {C}-{Mean} algorithm and entropy theory},
	volume = {126},
	issn = {0165-1684},
	url = {https://www.sciencedirect.com/science/article/pii/S0165168415003576},
	doi = {10.1016/j.sigpro.2015.10.014},
	abstract = {In data stream clustering studies, majority of methods are traditional hard clustering, the literatures of fuzzy clustering in clustering are few. In this paper, the fuzzy clustering algorithm is used to research data stream clustering, and the clustering results can truly reflect the actual relationship between objects and classes. It overcomes the either-or shortcoming of hard clustering. This paper presents a new method to detect concept drift. The membership degree of fuzzy clustering is used to calculate the information entropy of data, and according to the entropy to detect concept drift. The experimental results show that the detection of concept drift based on the entropy theory is effective and sensitive.},
	language = {en},
	urldate = {2022-07-29},
	journal = {Signal Processing},
	author = {Zhang, Baoju and Qin, Shan and Wang, Wei and Wang, Dan and Xue, Lei},
	month = sep,
	year = {2016},
	keywords = {Clustering, Concept drift detection, Entropy theory, Fuzzy C-Means},
	pages = {111--116},
}

{"_id":"BCG6PFKxiBiSBEum5","bibbaseid":"zhang-qin-wang-wang-xue-datastreamclusteringbasedonfuzzycmeanalgorithmandentropytheory-2016","author_short":["Zhang, B.","Qin, S.","Wang, W.","Wang, D.","Xue, L."],"bibdata":{"bibtype":"article","type":"article","series":"Signal Processing for Heterogeneous Sensor Networks","title":"Data stream clustering based on Fuzzy C-Mean algorithm and entropy theory","volume":"126","issn":"0165-1684","url":"https://www.sciencedirect.com/science/article/pii/S0165168415003576","doi":"10.1016/j.sigpro.2015.10.014","abstract":"In data stream clustering studies, majority of methods are traditional hard clustering, the literatures of fuzzy clustering in clustering are few. In this paper, the fuzzy clustering algorithm is used to research data stream clustering, and the clustering results can truly reflect the actual relationship between objects and classes. It overcomes the either-or shortcoming of hard clustering. This paper presents a new method to detect concept drift. The membership degree of fuzzy clustering is used to calculate the information entropy of data, and according to the entropy to detect concept drift. The experimental results show that the detection of concept drift based on the entropy theory is effective and sensitive.","language":"en","urldate":"2022-07-29","journal":"Signal Processing","author":[{"propositions":[],"lastnames":["Zhang"],"firstnames":["Baoju"],"suffixes":[]},{"propositions":[],"lastnames":["Qin"],"firstnames":["Shan"],"suffixes":[]},{"propositions":[],"lastnames":["Wang"],"firstnames":["Wei"],"suffixes":[]},{"propositions":[],"lastnames":["Wang"],"firstnames":["Dan"],"suffixes":[]},{"propositions":[],"lastnames":["Xue"],"firstnames":["Lei"],"suffixes":[]}],"month":"September","year":"2016","keywords":"Clustering, Concept drift detection, Entropy theory, Fuzzy C-Means","pages":"111–116","bibtex":"@article{zhang_data_2016,\n\tseries = {Signal {Processing} for {Heterogeneous} {Sensor} {Networks}},\n\ttitle = {Data stream clustering based on {Fuzzy} {C}-{Mean} algorithm and entropy theory},\n\tvolume = {126},\n\tissn = {0165-1684},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0165168415003576},\n\tdoi = {10.1016/j.sigpro.2015.10.014},\n\tabstract = {In data stream clustering studies, majority of methods are traditional hard clustering, the literatures of fuzzy clustering in clustering are few. In this paper, the fuzzy clustering algorithm is used to research data stream clustering, and the clustering results can truly reflect the actual relationship between objects and classes. It overcomes the either-or shortcoming of hard clustering. This paper presents a new method to detect concept drift. The membership degree of fuzzy clustering is used to calculate the information entropy of data, and according to the entropy to detect concept drift. The experimental results show that the detection of concept drift based on the entropy theory is effective and sensitive.},\n\tlanguage = {en},\n\turldate = {2022-07-29},\n\tjournal = {Signal Processing},\n\tauthor = {Zhang, Baoju and Qin, Shan and Wang, Wei and Wang, Dan and Xue, Lei},\n\tmonth = sep,\n\tyear = {2016},\n\tkeywords = {Clustering, Concept drift detection, Entropy theory, Fuzzy C-Means},\n\tpages = {111--116},\n}\n\n\n\n","author_short":["Zhang, B.","Qin, S.","Wang, W.","Wang, D.","Xue, L."],"key":"zhang_data_2016","id":"zhang_data_2016","bibbaseid":"zhang-qin-wang-wang-xue-datastreamclusteringbasedonfuzzycmeanalgorithmandentropytheory-2016","role":"author","urls":{"Paper":"https://www.sciencedirect.com/science/article/pii/S0165168415003576"},"keyword":["Clustering","Concept drift detection","Entropy theory","Fuzzy C-Means"],"metadata":{"authorlinks":{}},"html":""},"bibtype":"article","biburl":"https://bibbase.org/zotero/mh_lenguyen","dataSources":["iwKepCrWBps7ojhDx"],"keywords":["clustering","concept drift detection","entropy theory","fuzzy c-means"],"search_terms":["data","stream","clustering","based","fuzzy","mean","algorithm","entropy","theory","zhang","qin","wang","wang","xue"],"title":"Data stream clustering based on Fuzzy C-Mean algorithm and entropy theory","year":2016}