  Businesses, governmental bodies and NGO's have an ever-increasing amount of
data at their disposal from which they try to extract valuable information.
Often, this needs to be done not only accurately but also within a short time
frame. Clean and consistent data is therefore crucial. Data matching is the
field that tries to identify instances in data that refer to the same
real-world entity. In this study, machine learning techniques are combined with
string similarity functions to the field of data matching. A dataset of
invoices from a variety of businesses and organizations was preprocessed with a
grouping scheme to reduce pair dimensionality and a set of similarity functions
was used to quantify similarity between invoice pairs. The resulting invoice
pair dataset was then used to train and validate a neural network and a boosted
decision tree. The performance was compared with a solution from FISCAL
Technologies as a benchmark against currently available deduplication
solutions. Both the neural network and boosted decision tree showed equal to
better performance.
