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Software cost estimation based on modified K-Modes clustering Algorithm

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Abstract

Unsupervised technique like clustering may be used for software cost estimation in situations where parametric models are difficult to develop. This paper presents a software cost estimation model based on a modified K-Modes clustering algorithm. The aims of this paper are: first, the modified K-Modes clustering which is an enhancement over the simple K-Modes algorithm using a proper dissimilarity measure for mixed data types, is presented and second, the proposed K-Modes algorithm is applied for software cost estimation. We have compared our modified K-Modes algorithm with existing algorithms on different software cost estimation datasets, and results showed the effectiveness of our proposed algorithm.

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Authors and Affiliations

  1. Birla Institute of Technology, Ranchi, 834001, India

    Partha Sarathi Bishnu & Vandana Bhattacherjee

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  1. Partha Sarathi Bishnu
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  2. Vandana Bhattacherjee
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Correspondence to Partha Sarathi Bishnu.

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Bishnu, P.S., Bhattacherjee, V. Software cost estimation based on modified K-Modes clustering Algorithm. Nat Comput 15, 415–422 (2016). https://doi.org/10.1007/s11047-015-9492-7

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