Marketing and business intelligence with help of ant colony algorithm

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F26441021%3A_____%2F18%3AN0000005" target="_blank" >RIV/26441021:_____/18:N0000005 - isvavai.cz</a>

Result on the web

<a href="https://www.tandfonline.com/doi/full/10.1080/0965254X.2018.1430058" target="_blank" >https://www.tandfonline.com/doi/full/10.1080/0965254X.2018.1430058</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/0965254X.2018.1430058" target="_blank" >10.1080/0965254X.2018.1430058</a>

Result language

angličtina

Original language name

Marketing and business intelligence with help of ant colony algorithm

Original language description

Recently, there is increasing need of banks for targeting and acquiring new customers, for fraud detection in real time and for segmentation products through analysis of the customers. Doing it, they can serve their customers better, and can increase the effectiveness of the company. For this purpose, various data mining methods are used which enable extraction of interesting, nontrivial, implicit, previously unknown, and potentially useful patterns or knowledge from huge amounts of data. Traditional data mining methods include classification rule tasks, for their solution there are a number of methods. Among them can be mentioned, for example, Random forest algorithm or C4.5 algorithm. However, accuracy of these methods significantly reduces in the event that some data in databases is missing. These methods are always not optimal for very large databases. The aim of our work is to verify a possible solution of these problems by using the algorithm based on artificial ant colonies. This algorithm was successful in other areas. Therefore, we tested its applicability and accuracy in marketing and business intelligence and compared it with so far used methods. The experimental results showed that the presented algorithm is very effective, robust, and suitable for processing of very large files. It was also found that this algorithm overcomes the previously used algorithms in accuracy. Algorithm is easily implementable on different platforms and can be recommended for using in banking and business intelligence.

Czech name

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Czech description

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Type

J_ost - Miscellaneous article in a specialist periodical

CEP classification

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OECD FORD branch

50204 - Business and management

Project

—

Continuities

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Strategic Marketing

ISSN

1466-4488

e-ISSN

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Volume of the periodical

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Issue of the periodical within the volume

01 February 2018

Country of publishing house

GB - UNITED KINGDOM

Number of pages

13

Pages from-to

1-13

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-85041606819