TINJAUAN PUSTAKA SISTEMATIS PADA DATA MINING: STUDI KASUS ALGORITMA K-MEANS CLUSTERING
Keywords:
data mining, clustering, K-Means, systematic literature reviewAbstract
Data Mining is a method for analyzing future patterns and characteristics as well as gathering unexpected,
never-before-seen information from large databases. In data mining, clustering is one of the useful
techniques for analyzing data. One of the data mining algorithms is the K-Means algorithm, which is a
clustering technique based on distance division. The goal to be achieved in this paper is to analyze the
clustering technique using the K-Means algorithm in data mining by conducting an in-depth review and
searching through the literature selected based on the criteria and studies that will be selected to answer
research questions. Systematic Literature Review (SLR) is a method that aims to identify and find research
results with techniques based on specific procedures from comparison results. Based on the literature on
the selection of journal publications, Pattern Recognition, Knowledge-Based Systems, Applied Soft
Computing and IEEE Access can be the main references related to the K-Means algorithm. The results of
the comparison show that Euclidean Distance has the advantage of better distance calculation, so that this
method can be used as the main choice related to the calculation theory of the K-Means algorithm.
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