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Clustering Embedded with Context Awareness using an Evolutionary Approach

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Sanjeevani Dhaneshwar, Manisha R. Patil

Sanjeevani Dhaneshwar and Manisha R Patil. Clustering Embedded with Context Awareness using an Evolutionary Approach. International Journal of Computer Applications 146(5):1-5, July 2016. BibTeX

	author = {Sanjeevani Dhaneshwar and Manisha R. Patil},
	title = {Clustering Embedded with Context Awareness using an Evolutionary Approach},
	journal = {International Journal of Computer Applications},
	issue_date = {July 2016},
	volume = {146},
	number = {5},
	month = {Jul},
	year = {2016},
	issn = {0975-8887},
	pages = {1-5},
	numpages = {5},
	url = {},
	doi = {10.5120/ijca2016910689},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


The research presented in this paper explores the embedding of context awareness into a data mining method called clustering. Adding context to traditional data mining methods has been known to improve results of information retrieval systems. The approach used for this task is that of Multi Objective Evolutionary Algorithms. Evolutionary algorithms imitate the biological process of natural selection, also known as survival of the fittest, to solve computational problems. It is a heuristic method that finds approximate solutions. The solutions are generally optimized with respect to some system objective. However, many practical problems require optimization in more than one and possibly conflicting objectives. Multi Objective Evolutionary Algorithms (MOEA) are used for this purpose.


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Multi Objective Optimization, Evolutionary Algorithms, Data Mining, Clustering, Context Awareness