Mining Rank Data
This work addresses a gap in data mining for rank data, which is important for applications involving preferences or ordered lists, but it is incremental as it extends existing pattern mining techniques to a new data type.
The paper tackles the problem of mining rank data, which has been largely overlooked in data mining, by proposing algorithms for discovering frequent rankings and association rules between them, and demonstrates their effectiveness through experiments on synthetic and real datasets.
The problem of frequent pattern mining has been studied quite extensively for various types of data, including sets, sequences, and graphs. Somewhat surprisingly, another important type of data, namely rank data, has received very little attention in data mining so far. In this paper, we therefore addresses the problem of mining rank data, that is, data in the form of rankings (total orders) of an underlying set of items. More specifically, two types of patterns are considered, namely frequent rankings and dependencies between such rankings in the form of association rules. Algorithms for mining frequent rankings and frequent closed rankings are proposed and tested experimentally, using both synthetic and real data.