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A proposed hybrid clustering algorithm using K-means and BIRCH for cluster based cab recommender system (CBCRS)

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Abstract

An efficient Cluster Based Cab Recommender System (CBCRS) assists the cab drivers with the recommendations about passenger pickup location available at the shortest distance from him. To recommend drivers about the passenger pickup location, one need to group the Global Positioning System (GPS) coordinates of several pickup points of the same geographic region. The GPS coordinates of cab pick-up points are unsupervised data. Clustering of unsupervised cab dataset is troublesome since cab dataset is a large database and clustering techniques when applied on such large datasets do not generate good clusters for GPS datapoints. Therefore, this research paper proposes an improved hybrid clustering algorithm which combines the features of Partition-based clustering and Hierarchical Based Clustering techniques. Thus, the objectives of the research paper are four folds: firstly, the research paper identifies various clustering techniques to cluster GPS Coordinates. Secondly, to design and develop an improved hybrid clustering algorithm for CBCRS. Thirdly, the research paper compares the clusters formed by the proposed algorithm with standard K-Means and Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) using three datasets over Silhouette Coefficient and Calinski-Harabasz Score. Finally, the paper concludes and analyses the results of the proposed algorithm.

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Authors and Affiliations

  1. Panjab University, Chandigarh, Panjab, India

    Supreet Kaur Mann & Sonal Chawla

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  1. Supreet Kaur Mann
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  2. Sonal Chawla
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Correspondence to Supreet Kaur Mann.

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Mann, S.K., Chawla, S. A proposed hybrid clustering algorithm using K-means and BIRCH for cluster based cab recommender system (CBCRS). Int. j. inf. tecnol. 15, 219–227 (2023). https://doi.org/10.1007/s41870-022-01113-6

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