Comparative Analysis of Mining Transactional and Time Series Data
T. Muralidharan, S. P. Rajagopalan · CiiT international journal of data mining and knowledge engineering · 2015
Web sites and transactional databases collect large amounts of time-stamped data related to an organization’s suppliers and/or customers over time. Mining these time-stamped data can help business leaders make better decisions by listening to their suppliers or customers via their transactions collected over time. A business can have many suppliers and/or customers and may have a set of transactions associated with each one. However, the size of each set of transactions may be quite large, making it difficult to perform many traditional data-mining tasks. This paper proposes techniques for large-scale reduction of time-stamped data using time series analysis, seasonal decomposition, and automatic time series model selection. After data reduction, traditional data mining techniques can then be applied to the reduced data along with other profile data. This paper demonstrates these techniques SAS® High-Performance Forecasting software.