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Proceedings of the 4th International Conference on Frontiers in Intelligent Computing: Theory and Applications (FICTA) 2015 pp 273–283Cite as

A Novel Combined Approach of k-Means and Genetic Algorithm to Cluster Cultural Goods in Household Budget

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 404))

Abstract

Setting up household spending and leading to an efficient and optimal usage is one of the important issues that every family faces. In this paper, we consider a sample of 35,000 households, among them we evaluate features of households that allocated a larger share of the budget to cultural goods and cluster them to extract common social and economic characteristics using Combination of k-means and genetic algorithm. GA as a meta-heuristic optimization algorithm increases the speed of achieving optimal solutions in k-means algorithm. The families’ priorities to spend their budget in rural and urban areas show that in most of the families with a high level portion of cultural goods, food and drinks, smokes, and education are three categories which have a higher priority than other groups. The results show the highest accuracy that there are three well separated and compact clusters, which for the fitness are accredited by Davies–Bouldin index by calculating inter and intra distances.

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Author information

Authors and Affiliations

  1. Department of Knowledge Engineering and Decision Science, Kharazmi University, #242, Somayeh Street, Between Qarani and Vila, Tehran, Iran

    Sara Sadat Babaie

  2. Department of Mathematics and Computer Science, Kharazmi University, #242, Somayeh Street, Between Qarani and Vila, Tehran, Iran

    Ebadati E. Omid Mahdi

  3. Faculty of Management, Kharazmi University, #242, Somayeh Street, Between Qarani and Vila, Tehran, Iran

    Tohid Firoozan

Authors
  1. Sara Sadat Babaie
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  2. Ebadati E. Omid Mahdi
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  3. Tohid Firoozan
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Corresponding author

Correspondence to Ebadati E. Omid Mahdi .

Editor information

Editors and Affiliations

  1. Machine Intelligence Unit, ISI, Kolkata, West Bengal, India

    Swagatam Das

  2. Computer Science and Engineering, National Institute of Technology, Durgapur, West Bengal, India

    Tandra Pal

  3. National Institute of Technology, Durgapur, West Bengal, India

    Samarjit Kar

  4. Deparment of CSE, Anil Neerukonda Ins. of Tech. & Sci., Vishakapatnam, India

    Suresh Chandra Satapathy

  5. Kalyani University, Nadia, West Bengal, India

    Jyotsna Kumar Mandal

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Babaie, S.S., Omid Mahdi, E.E., Firoozan, T. (2016). A Novel Combined Approach of k-Means and Genetic Algorithm to Cluster Cultural Goods in Household Budget. In: Das, S., Pal, T., Kar, S., Satapathy, S., Mandal, J. (eds) Proceedings of the 4th International Conference on Frontiers in Intelligent Computing: Theory and Applications (FICTA) 2015. Advances in Intelligent Systems and Computing, vol 404. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2695-6_24

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  • DOI: https://doi.org/10.1007/978-81-322-2695-6_24

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2693-2

  • Online ISBN: 978-81-322-2695-6

  • eBook Packages: EngineeringEngineering (R0)

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