Tuesday, September 17, 9:30am
Sarkey Energy Center, Conference Room C (Room 360)
Phenomenal data mining finds relations between the data and the phenomena that give rise to data rather than just relations among the data. For example, suppose supermarket cash register data does not identify cash customers. Nevertheless, there really are customers, and a data mining program might be able to identify which baskets of purchases are likely to have been made by the same customers. In this example, the receipts are the data, and the customers are phenomena not directly represented in the data. Once the ``baskets'' of purchases are grouped by customer, the way is open to find more facts about the customers. This article concerns what can be inferred about phenomena from data and what facts are relevant to doing this. We work mainly with the supermarket example, but the idea is general. The main technical point is that functions and predicates involving the phenomena should be explicit in the logical sentences and not just present in the mind of the person doing the data mining.