Big data research has become an important discipline in information systems research.
However, the flood of data being generated on the Internet is increasingly unstructured
and non-numeric in the form of images and texts. Thus, research indicates that there is an
increasing need to develop more efficient algorithms for treating mixed data in big data for
effective decision making. In this paper, we apply the classical K-means algorithm to both
numeric and categorical attributes in big data platforms. We first present an algorithm that
handles the problem of mixed data. We then use big data platforms to implement the algorithm, demonstrating its functionalities by applying the algorithm in a detailed case study.
This provides us with a solid basis for performing more targeted profiling for decision
making and research using big data. Consequently, the decision makers will be able to
treat mixed data, numerical and categorical data, to explain and predict phenomena in the
big data ecosystem. Our research includes a detailed end-to-end case study that presents
an implementation of the suggested procedure. This demonstrates its capabilities and the
advantages that allow it to improve the decision-making process by targeting organizations’ business requirements to a specific cluster[s]/profiles[s] based on the enhancement
outcomes.
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