Learning from Order Examples

We advocate a new learning task that deals with orders of items, and we call this the Learning from Order Examples (LOE) task. The aim of the task is to acquire the rule that is used for estimating the proper order of a given unordered item set. The rule is acquired from training examples that are ordered item sets. We present several solution methods for this task, and evaluate the performance and the characteristics of these methods based on the experimental results of tests using both artificial data and realistic data.

• Type: Conference
• Year: 2002
• Where: ICDM
• Authors: Toshihiro Kamishima, Shotaro Akaho
• Keywords: Data Mining, ICDM 2002, Item Sets, Order Examples, Unordered Item