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Autonomous Market Segments Estimation Using Density Conscious Artificial Immune System Learner

Autonomous Market Segments Estimation Using Density Conscious Artificial Immune System Learner

Vishwambhar Pathak
Copyright: © 2017 |Pages: 26
ISBN13: 9781522522348|ISBN10: 1522522344|EISBN13: 9781522522355
DOI: 10.4018/978-1-5225-2234-8.ch006
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MLA

Pathak, Vishwambhar. "Autonomous Market Segments Estimation Using Density Conscious Artificial Immune System Learner." Maximizing Business Performance and Efficiency Through Intelligent Systems, edited by Om Prakash Rishi and Anukrati Sharma, IGI Global, 2017, pp. 110-135. https://doi.org/10.4018/978-1-5225-2234-8.ch006

APA

Pathak, V. (2017). Autonomous Market Segments Estimation Using Density Conscious Artificial Immune System Learner. In O. Rishi & A. Sharma (Eds.), Maximizing Business Performance and Efficiency Through Intelligent Systems (pp. 110-135). IGI Global. https://doi.org/10.4018/978-1-5225-2234-8.ch006

Chicago

Pathak, Vishwambhar. "Autonomous Market Segments Estimation Using Density Conscious Artificial Immune System Learner." In Maximizing Business Performance and Efficiency Through Intelligent Systems, edited by Om Prakash Rishi and Anukrati Sharma, 110-135. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-2234-8.ch006

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Abstract

Automated exploration of groups of customers to understand customer behavior from raw data is highly required to support strategic decision making given the pressure of competitive market. Several mathematical and statistical methods have been applied for autonomous model estimation from multivariate data. The current paper investigates employability of new generation of bio-inspired metaheuristic algorithms, named the artificial immune system (AIS), which in the current proposition, learn through density based kernels. As such the model simulates probabilistic behavior of the dendritic cells (DCs) during recognition of the antigens and danger signals, whose learning has been modeled with an infinite Gaussian mixture model. The unsupervised learning capability of the model has been found to be effective for multivariate data.

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