Maintaining Knowledge-Bases of Navigational Patterns from Streams of Navigational Sequences Ajumobi Udechukwu, Ken Barker, and Reda Alhajj   View Paper (PDF)   Return to Applications of Stream Data Mining Abstract In this paper we explore an alternative design goal for navigational pattern discovery in stream environments. Instead of mining based on thresholds and returning the patterns that satisfy the specified threshold(s), we propose to mine without thresholds and return all identified patterns along with their support counts in a single pass. We utilize a sliding window to capture recent navigational sequences and propose a batch-update strategy for maintaining the patterns within a sliding window. Our batch-update strategy depends on the ability to efficiently mine the navigational patterns without support thresholds. To achieve this, we have designed an efficient algorithm for mining contiguous navigational patterns without support thresholds. Our experiments show that our algorithm outperforms the existing techniques for mining contiguous navigational patterns. Our experiments also show that the proposed batch-update strategy achieves considerable speed-ups compared to the existing window update strategy, which requires total re-computation of patterns within each new window. ©2006 Association for Computing Machinery