Introduction: An important chain of supermarkets in the western zone of the capital of Chile, needs to obtain key information to make decisions, this information is available in the databases but needs to be processed due to the complexity and quantity of information which becomes difficult to visualiz,. Method: For this purpose, an algorithm was developed using artificial neural networks applying Kohonen's SOM method. To carry it out, certain key procedures must be followed to develop it, such as data mining that will be responsible for filtering and then use only the relevant data for market basket analysis. After filtering the information, the data must be prepared. After data preparation, we prepared the Python programming environment to adapt it to the sample data, then proceed to train the SOM with its parameters set after test results. Result: the result of the SOM obtains the relationship between the products that were most purchased by positioning them topologically close, to form promotions, packs and bundles for the retail manager to take into consideration, because these relationships were obtained as a result of the SOM training with the real transactions of the clients. Conclusion: Based on this, recommendations on frequent shopping baskets have been made to the supermarket chain that provided the data used in the research

中文翻译：

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Análisis de Canasta de mercado en supermercados mediumte mapas auto-organizados

简介：智利首都西部地区的一家重要连锁超市，需要获取关键信息进行决策，这些信息在数据库中是可用的，但由于信息的复杂性和数量变得难以处理，因此需要进行处理。可视化，。方法：为此，使用人工神经网络应用 Kohonen 的 SOM 方法开发了一种算法。要执行它，必须遵循某些关键程序来开发它，例如负责过滤的数据挖掘，然后仅将相关数据用于市场篮子分析。过滤信息后，必须准备数据。数据准备好后，我们准备了Python编程环境使其适应样本数据，然后根据测试结果设置参数继续训练SOM。结果：SOM 的结果通过将它们放置在拓扑上接近的位置来获得购买最多的产品之间的关系，形成促销、包装和捆绑供零售经理考虑，因为这些关系是作为 SOM 的结果获得的对客户的真实交易进行培训。结论：在此基础上，向提供研究数据的连锁超市提出了频繁购物篮的建议