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Clustering Based Algorithmic Design for Cab Recommender System (CRS)

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ICT Analysis and Applications

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 93))

Abstract

An efficient Cab Recommender System (CRS) assists the cab drivers with the shortest distance for the next passenger location. For this, it becomes imperative for a CRS to generate clusters for Geolocations. Clustering of Geolocations faces major challenges like noise, identification of meaningful clusters, semantic locations, etc. Therefore, the objectives of this research paper are fourfolds. Firstly, to extensively review the literature for Geolocations and identify the existent clustering techniques. Secondly, to propose an algorithm for generating clusters for Geolocations. Thirdly, to implement and test the proposed algorithm on standard dataset pertaining to different clustering techniques and finally, to analyze and compare the results of the proposed algorithm for effective clustering of Geolocations.

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Author information

Authors and Affiliations

  1. Panjab University, Chandigarh, India

    Supreet Kaur Mann & Sonal Chawla

Authors
  1. Supreet Kaur Mann
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  2. Sonal Chawla
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Corresponding author

Correspondence to Supreet Kaur Mann .

Editor information

Editors and Affiliations

  1. University of Macau, Macau, China

    Simon Fong

  2. Techno India College of Engineering, Kolkata, India

    Nilanjan Dey

  3. Ahmedabad, India

    Amit Joshi

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Mann, S.K., Chawla, S. (2020). Clustering Based Algorithmic Design for Cab Recommender System (CRS). In: Fong, S., Dey, N., Joshi, A. (eds) ICT Analysis and Applications. Lecture Notes in Networks and Systems, vol 93. Springer, Singapore. https://doi.org/10.1007/978-981-15-0630-7_35

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  • DOI: https://doi.org/10.1007/978-981-15-0630-7_35

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0629-1

  • Online ISBN: 978-981-15-0630-7

  • eBook Packages: EngineeringEngineering (R0)

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