@inproceedings{hassani_density-based_2012,
	address = {Berlin, Heidelberg},
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {Density-{Based} {Projected} {Clustering} of {Data} {Streams}},
	isbn = {978-3-642-33362-0},
	doi = {10.1007/978-3-642-33362-0_24},
	abstract = {In this paper, we have proposed, developed and experimentally validated our novel subspace data stream clustering, termed PreDeConStream. The technique is based on the two phase mode of mining streaming data, in which the first phase represents the process of the online maintenance of a data structure, that is then passed to an offline phase of generating the final clustering model. The technique works on incrementally updating the output of the online phase stored in a micro-cluster structure, taking into consideration those micro-clusters that are fading out over time, speeding up the process of assigning new data points to existing clusters. A density based projected clustering model in developing PreDeConStream was used. With many important applications that can benefit from such technique, we have proved experimentally the superiority of the proposed methods over state-of-the-art techniques.},
	language = {en},
	booktitle = {Scalable {Uncertainty} {Management}},
	publisher = {Springer},
	author = {Hassani, Marwan and Spaus, Pascal and Gaber, Mohamed Medhat and Seidl, Thomas},
	editor = {Hüllermeier, Eyke and Link, Sebastian and Fober, Thomas and Seeger, Bernhard},
	year = {2012},
	pages = {311--324},
}

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