Mining Compressing Patterns in a Data Stream

Mining patterns that compress the data well was shown to be an effective approach for extracting meaningful patterns and solving the redundancy issue in frequent pattern mining. Most of the existing works in the literature consider mining compressing patterns from a static database of itemsets or sequences. These approaches require multiple passes through the data and do not scale up with the size of data streams. In this paper, we study the problem of mining compressing sequential patterns from a data stream. We propose an approximate algorithm that needs only a single pass through the data and efficiently extracts a meaningful and non-redundant set of sequential patterns. Experiments on three synthetic and three real-world large-scale datasets show that our approach extracts meaningful compressing patterns as the state-of-the-art multi-pass algorithms proposed for static databases of sequences. Moreover, our approach scales linearly with the size of data streams while all the state-of-the-art algorithms do not.