Data Analysis of Marketing Companies using Improved K-Means Clustering and LRFMM2 Model
Subject Areas : Computer Engineering and ITAtieh Mirzaei 1 , zahra rezaei 2
1 - Department of Computer Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
2 - Department of Computer Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran
Keywords: Clustering, K-means, Optimization, Marketing, Analysis,
Abstract :
Clustering, especially k-means, is one of the most important data mining techniques for identifying and monitoring customer behavior. In classical k-means, the optimality depends on the initial selection of the centers; therefore, it is not optimal. Another problem is determining the number of clusters and making the clusters zero. Customers’ behavioral characteristics are considered in customer clustering, and a method is used to find the optimal number of clusters and the initial values of cluster centers to achieve more accurate results and predict customer lifetime. The results of this research show the customer behavior of each cluster. The proposed improved k-means method has been compared with the classical K-means once including the M2 parameter (customer satisfaction level) and once without M2. The Normalized Mutual Information (NMI) criterion was calculated on the proposed method and the compared method, and in both cases, considering or missing the M2 index, the NMI of the proposed method was higher. Also, the adjusted rand index with the M2 parameter has recorded the highest number. In terms of time, it is faster than classical K-means, which shows that the proposed method has the best performance in terms of speed and performance accuracy compared to classical K-means.
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