Sequence Data Mining

Abstract

Many interesting real-life mining applications rely on modeling data as sequences of discrete multi-attribute records. Existing literature on sequence mining is partitioned on application-specific boundaries. In this article we distill the basic operations and techniques that are common to these applications. These include conventional mining operations, such as classification and clustering, and sequence specific operations, such as tagging and segmentation. We review state-of-the-art techniques for sequential labeling and show how these apply in two real-life applications arising in address cleaning and information extraction from websites.