Unsupervised anomaly detection for structured data - Finding similarities between retail products

Abstract: Data is one of the most contributing factors for modern business operations. Having bad data could therefore lead to tremendous losses, both financially and for customer experience. This thesis seeks to find anomalies in real-world, complex, structured data, causing an international enterprise to miss out on income and the potential loss of customers. By using graph theory and similarity analysis, the findings suggest that certain countries contribute to the discrepancies more than other countries. This is believed to be an effect of countries customizing their products to match the market’s needs. This thesis is just scratching the surface of the analysis of the data, and the number of opportunities for future work are therefore many.