The identification and extraction of itemset support defined by the weight matrix of a Self-Organising Map

Frequent Itemset Mining, which is the core of Association Rule Mining, is a very well-known problem in the data mining field. Similarly, a Self-Organising Map is a well known neural network which has been used for data clustering mainly. In the discovery of frequent itemsets, conforming the raw material to create association rules, the support, being an itemset metric, is highly important since it determines the interestingness of any itemset in a mining process. In this work, we propose and define a probabilistic method to identify and extract from the weight matrix of a trained map the support of all of the possible itemsets that can be formed by the components of the patterns in the training dataset.