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Clustering such data is constrained by incompleteness of data, data distribution, and continuous nature of data streams. Ignoring missing values in incomplete data clustering, especially in high missing rates decreases the clustering performance. Traditional clustering is applied on the whole data without dealing with data distribution. This paper presents an efficient framework called Fuzzy c-means clustering for Incomplete Data streams (FID) that works adaptively with incomplete data streams even with high missing rates. The proposed FID estimates missing values based on the corresponding nearest-neighbors\u2019 intervals. To overcome the previously mentioned data streams clustering problems, the continuous clustering mechanism is adopted and extended to accurately handle the incomplete data streams. Experimental results using two different data sets prove the efficiency of the proposed FID comparing to the alternative approaches.<\/jats:p>","DOI":"10.3233\/jifs-191184","type":"journal-article","created":{"date-parts":[[2020,1,24]],"date-time":"2020-01-24T12:20:53Z","timestamp":1579868453000},"page":"3213-3227","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Clustering based approach for incomplete data streams processing"],"prefix":"10.1177","volume":"38","author":[{"given":"Fatma M.","family":"Najib","sequence":"first","affiliation":[{"name":"Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rasha M.","family":"Ismail","sequence":"additional","affiliation":[{"name":"Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nagwa L.","family":"Badr","sequence":"additional","affiliation":[{"name":"Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tarek F.","family":"Gharib","sequence":"additional","affiliation":[{"name":"Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2020,1,22]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1109\/SAMPTA.2015.7148933","article-title":"Greedy algorithm for subspace clustering from corrupted and incomplete data","author":"Petukhov A.","year":"2015","unstructured":"PetukhovA. and KozlovI., Greedy algorithm for subspace clustering from corrupted and incomplete data, In International Conference on Sampling Theory and Applications (SampTA), IEEE, 2015, pp. 458\u2013462.","journal-title":"International Conference on Sampling Theory and Applications (SampTA)"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10846-014-0152-4"},{"issue":"11","key":"e_1_3_1_4_2","doi-asserted-by":"crossref","first-page":"1649","DOI":"10.1093\/comjnl\/bxt075","article-title":"Recognizing daily and sports activities in two open source machine learning environments using body-worn sensor units","volume":"57","author":"Barshan B.","year":"2014","unstructured":"BarshanB. and Y\u00fcksekM.C., Recognizing daily and sports activities in two open source machine learning environments using body-worn sensor units, The Computer Journal 57(11) (2014), 1649\u20131667.","journal-title":"The Computer Journal"},{"key":"e_1_3_1_5_2","first-page":"1","article-title":"Dynamically updating approximations based on multi-threshold tolerance relation in incomplete interval-valued decision information systems","author":"Lin B.","year":"2019","unstructured":"LinB., ZhangX., XuW. and WuY., Dynamically updating approximations based on multi-threshold tolerance relation in incomplete interval-valued decision information systems, Knowledge and Information Systems (2019), 1\u201325.","journal-title":"Knowledge and Information Systems"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-169747"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-014-0288-3"},{"issue":"13","key":"e_1_3_1_8_2","doi-asserted-by":"crossref","first-page":"4185","DOI":"10.1007\/s00500-017-2708-2","article-title":"Improved clustering algorithm based on high-speed network data stream","volume":"22","author":"Yin C.","year":"2018","unstructured":"YinC., XiaL., ZhangS., SunR. and WangJ., Improved clustering algorithm based on high-speed network data stream, Soft Computing 22(13) (2018), 4185\u20134195.","journal-title":"Soft Computing"},{"key":"e_1_3_1_9_2","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.eswa.2017.10.026","article-title":"Batch-based active learning: Application to social media data for crisis management","volume":"93","author":"Pohl D.","year":"2018","unstructured":"PohlD., BouchachiaA. and HellwagnerH., Batch-based active learning: Application to social media data for crisis management, Expert SystAppl 93 (2018), 232\u2013244.","journal-title":"Expert SystAppl"},{"key":"e_1_3_1_10_2","first-page":"1","article-title":"Artificial Neural Networks with Random Weights for Incomplete Datasets","author":"Mesquita D.P.","year":"2019","unstructured":"MesquitaD.P., GomesJ.P.P. and RodriguesL.R., Artificial Neural Networks with Random Weights for Incomplete Datasets, Neural Processing Letters (2019), 1\u201328.","journal-title":"Neural Processing Letters"},{"key":"e_1_3_1_11_2","unstructured":"Daily and Sports Activities Data Set http:\/\/archive.ics.uci.edu\/ml\/datasets\/Daily+and+Sports+Activities."},{"key":"e_1_3_1_12_2","unstructured":"DuaD. 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