Clustering attribute values in transitional data streams

dc.contributor.author	Naik, S.B.
dc.contributor.author	Pawar, J.D.
dc.date.accessioned	2018-10-09T09:29:19Z
dc.date.available	2018-10-09T09:29:19Z
dc.date.issued	2017
dc.identifier.citation	International Conference on Computing, Communication and Automation (ICCCA), 5-6 May 2017. 2017; 58-62.	en_US
dc.identifier.uri	http://dx.doi.org/10.1109/CCAA.2017.8229771
dc.identifier.uri	http://irgu.unigoa.ac.in/drs/handle/unigoa/5438
dc.description.abstract	Millions of users create user profiles on social media. Changes made to an attribute in the user profiles on social media generate a huge volume of data representing a data stream. A framework has been proposed to analyze such data streams and cluster the attribute values related to each other.	en_US
dc.publisher	IEEE	en_US
dc.subject	Computer Science and Technology	en_US
dc.title	Clustering attribute values in transitional data streams	en_US
dc.type	Conference article	en_US
dc.identifier.impf	cs