Associative prediction model and clustering for product forecast data

Abstract

Association rules are adopted to discover the interesting relationship and knowledge in a large dataset. Knowledge may appear in terms of a frequent pattern discovered in a large number of production data. This knowledge can improve or solve production problems to achieve low cost production. To obtain knowledge and quality information, data mining can be applied to the manufacturing industry. In this study, we used one of the association rule approach, i.e. Apriori algorithm to build an associative prediction model for product forecast data. Also, we adopt the simplest method in clustering, k-means algorithm to attain the link between patterns. The real industrial product forecast data for one year duration is used in the experiment. This data consists of 42 products with two important attributes, i.e. time in the week and required quantity. Since the data mining processes need a large amount of data, we simulated these data by using the Monte Carlo technique to obtain another 15 years of simulated forecast data. There are two main experiments for the association rules mining and clustering. As a result, we obtain an associative prediction model and clustering for the forecasting data. The extracted model provides the prediction knowledge about the range of production in a certain period.