Call for Paper - November 2023 Edition
IJCA solicits original research papers for the November 2023 Edition. Last date of manuscript submission is October 20, 2023. Read More

Clustering Embedded with Context Awareness using an Evolutionary Approach

Print
PDF
 
Download Full Text
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 146 - Number 5
Year of Publication: 2016
Authors:
Sanjeevani Dhaneshwar, Manisha R. Patil
10.5120/ijca2016910689

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

@article{10.5120/ijca2016910689,
	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 = {http://www.ijcaonline.org/archives/volume146/number5/25391-2016910689},
	doi = {10.5120/ijca2016910689},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

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.

References

  1. Gediminas Adomavicius, Bamshad Mobasher, Francesco Ricci, Alex Tuzhilin, Context Aware Recommender Systems, AI MAGAZINE Fall 2011. ISSN 0738-4602.
  2. A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing, Chapter 2, Springer, Natural Computing Series 1st edition, 2003, ISBN: 3-540-40184-9 Corr. 2nd printing, 2007, ISBN: 978-3-540-40184-1
  3. Anirban Mukhopadhyay, Ujjwal Maulik, Sanghamitra Bandyopadhyay, Carlos A. Coello Coello. Survey of Multiobjective Evolutionary Algorithms for Data Mining: Part II. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, VOL. 18, NO. 1, FEBRUARY 2014
  4. Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and T. Meyarivan , A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II, IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, VOL. 6, NO. 2, APRIL 2002
  5. Abdullah Konak, David W. Coit, Alice E. Smith. Multi-Objective Optimization Using Genetic Algorithms: A Tutorial.
  6. Boriah, Shyam, Varun Chandola, and Vipin Kumar. "Similarity measures for categorical data: A comparative evaluation." red 30.2 (2008): 3.
  7. Ming-Hseng Tseng, Chang-Yun Chiang, Ping-Hung Tang, Hui-Ching Wu, A STUDY ON CLUSTER VALIDITY USING INTELLIGENT EVOLUTIONARY K-MEANS APPROACH, Proceedings of the Ninth International Conference on Machine Learning and Cybernetics, Qingdao, 11-14 July 2010.
  8. Marcheggiani, D., Täckström, O., Esuli, A, Sebastiani, F.: Hierarchical Multi-Label Conditional Random Fields for Aspect-Oriented Opinion Mining. In: Proceedings of the 36th European Conference on Information Retrieval (ECIR 2014).
  9. Yi Zuo, Maoguo Gong, Jiulin Zeng, Lijia Ma, and Licheng Jiao. Personalized Recommendation Based on Evolutionary Multi-Objective Optimization. IEEE Computational intelligence magazine | February 2015

Keywords

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