A Fuzzy Density-based Clustering Algorithm for Streaming Data. Aliperti, A., Bechini, A., Marcelloni, F., & Renda, A. In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pages 1–6, June, 2019. ISSN: 1558-4739doi abstract bibtex The exploitation of data streams, nowadays provided nonstop by a myriad of diverse applications, asks for specific analysis methods. In this paper, we propose SF-DBSCAN, a fuzzy version of the DBSCAN algorithm, aimed to perform unsupervised analysis of streaming data. Fuzziness is introduced by fuzzy borders of density-based clusters. We describe and discuss the proposed algorithm, which evolves the clusters at each occurrence of a new object. Three synthetic datasets are used to show the ability of SF-DBSCAN to successfully track changes of data distribution, thus properly addressing concept drift. SF-DBSCAN is compared with a basic, crisp streaming version of DBSCAN with regard to modelling effectiveness.
@inproceedings{aliperti_fuzzy_2019,
title = {A {Fuzzy} {Density}-based {Clustering} {Algorithm} for {Streaming} {Data}},
doi = {10.1109/FUZZ-IEEE.2019.8858909},
abstract = {The exploitation of data streams, nowadays provided nonstop by a myriad of diverse applications, asks for specific analysis methods. In this paper, we propose SF-DBSCAN, a fuzzy version of the DBSCAN algorithm, aimed to perform unsupervised analysis of streaming data. Fuzziness is introduced by fuzzy borders of density-based clusters. We describe and discuss the proposed algorithm, which evolves the clusters at each occurrence of a new object. Three synthetic datasets are used to show the ability of SF-DBSCAN to successfully track changes of data distribution, thus properly addressing concept drift. SF-DBSCAN is compared with a basic, crisp streaming version of DBSCAN with regard to modelling effectiveness.},
booktitle = {2019 {IEEE} {International} {Conference} on {Fuzzy} {Systems} ({FUZZ}-{IEEE})},
author = {Aliperti, Andrea and Bechini, Alessio and Marcelloni, Francesco and Renda, Alessandro},
month = jun,
year = {2019},
note = {ISSN: 1558-4739},
keywords = {Clustering algorithms, Data structures, Memory management, Partitioning algorithms, Proposals, Sensitivity, Shape},
pages = {1--6},
}
Downloads: 0
{"_id":"HrEbviJXXqDvQfn5P","bibbaseid":"aliperti-bechini-marcelloni-renda-afuzzydensitybasedclusteringalgorithmforstreamingdata-2019","author_short":["Aliperti, A.","Bechini, A.","Marcelloni, F.","Renda, A."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","title":"A Fuzzy Density-based Clustering Algorithm for Streaming Data","doi":"10.1109/FUZZ-IEEE.2019.8858909","abstract":"The exploitation of data streams, nowadays provided nonstop by a myriad of diverse applications, asks for specific analysis methods. In this paper, we propose SF-DBSCAN, a fuzzy version of the DBSCAN algorithm, aimed to perform unsupervised analysis of streaming data. Fuzziness is introduced by fuzzy borders of density-based clusters. We describe and discuss the proposed algorithm, which evolves the clusters at each occurrence of a new object. Three synthetic datasets are used to show the ability of SF-DBSCAN to successfully track changes of data distribution, thus properly addressing concept drift. SF-DBSCAN is compared with a basic, crisp streaming version of DBSCAN with regard to modelling effectiveness.","booktitle":"2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","author":[{"propositions":[],"lastnames":["Aliperti"],"firstnames":["Andrea"],"suffixes":[]},{"propositions":[],"lastnames":["Bechini"],"firstnames":["Alessio"],"suffixes":[]},{"propositions":[],"lastnames":["Marcelloni"],"firstnames":["Francesco"],"suffixes":[]},{"propositions":[],"lastnames":["Renda"],"firstnames":["Alessandro"],"suffixes":[]}],"month":"June","year":"2019","note":"ISSN: 1558-4739","keywords":"Clustering algorithms, Data structures, Memory management, Partitioning algorithms, Proposals, Sensitivity, Shape","pages":"1–6","bibtex":"@inproceedings{aliperti_fuzzy_2019,\n\ttitle = {A {Fuzzy} {Density}-based {Clustering} {Algorithm} for {Streaming} {Data}},\n\tdoi = {10.1109/FUZZ-IEEE.2019.8858909},\n\tabstract = {The exploitation of data streams, nowadays provided nonstop by a myriad of diverse applications, asks for specific analysis methods. In this paper, we propose SF-DBSCAN, a fuzzy version of the DBSCAN algorithm, aimed to perform unsupervised analysis of streaming data. Fuzziness is introduced by fuzzy borders of density-based clusters. We describe and discuss the proposed algorithm, which evolves the clusters at each occurrence of a new object. Three synthetic datasets are used to show the ability of SF-DBSCAN to successfully track changes of data distribution, thus properly addressing concept drift. SF-DBSCAN is compared with a basic, crisp streaming version of DBSCAN with regard to modelling effectiveness.},\n\tbooktitle = {2019 {IEEE} {International} {Conference} on {Fuzzy} {Systems} ({FUZZ}-{IEEE})},\n\tauthor = {Aliperti, Andrea and Bechini, Alessio and Marcelloni, Francesco and Renda, Alessandro},\n\tmonth = jun,\n\tyear = {2019},\n\tnote = {ISSN: 1558-4739},\n\tkeywords = {Clustering algorithms, Data structures, Memory management, Partitioning algorithms, Proposals, Sensitivity, Shape},\n\tpages = {1--6},\n}\n\n\n\n","author_short":["Aliperti, A.","Bechini, A.","Marcelloni, F.","Renda, A."],"key":"aliperti_fuzzy_2019","id":"aliperti_fuzzy_2019","bibbaseid":"aliperti-bechini-marcelloni-renda-afuzzydensitybasedclusteringalgorithmforstreamingdata-2019","role":"author","urls":{},"keyword":["Clustering algorithms","Data structures","Memory management","Partitioning algorithms","Proposals","Sensitivity","Shape"],"metadata":{"authorlinks":{}},"html":""},"bibtype":"inproceedings","biburl":"https://bibbase.org/zotero/mh_lenguyen","dataSources":["iwKepCrWBps7ojhDx"],"keywords":["clustering algorithms","data structures","memory management","partitioning algorithms","proposals","sensitivity","shape"],"search_terms":["fuzzy","density","based","clustering","algorithm","streaming","data","aliperti","bechini","marcelloni","renda"],"title":"A Fuzzy Density-based Clustering Algorithm for Streaming Data","year":2019}