Insurance fraud costs the property and casualty insurance industry over 25 billion dollars (USD) annually. This paper addresses workers' compensation claim fraud. A data mining approach is adopted, and issues of data preparation are discussed. The focus is on building predictive models to score an open claim for a propensity to be fraudulent. A key component to modeling is the use of textual da...

Classification is a data mining technique which is utilized to predict the future by using available data and aims to discover hidden relationships between variables and classes. Since the cost component is crucial in most real life classification problems and most traditional classification methods work for the purpose of correct classification, developing cost-sensitive classifiers which mini...

In the area of process mining, the ILP Miner is known for the fact that it always returns a Petri net that perfectly fits a given event log. However, the downside of the ILP Miner is that its complexity is exponential in the number of event classes in that event log. As a result, the ILP Miner may take a very long time to return a Petri net. Partitioning the traces in the event log over multipl...

This paper shows how a rational Bitcoin miner should select transactions from his node’s mempool, when creating a new block, in order to maximize his profit in the absence of a block size limit. To show this, the paper introduces the block space supply curve and the mempool demand curve. The former describes the cost for a miner to supply block space by accounting for orphaning risk. The latter...

Fuzzy logic and genetic algorithms are well-established computational techniques that have been employed to deal with the problem of classification as this is presented in the context of data mining. Based on Fuzzy Miner which is a recently proposed state-of-the-art fuzzy rule based system for numerical data, in this paper we propose GF-Miner which is a genetic fuzzy classifier that improves Fu...