An Efficient Algorithm for Finding Frequent Sequential Traversal Patterns Mining With Weight Constraint Using Web Log Pattern

Abstract

Numerous methods for mining frequent sequential traversal trends have been created. These algorithms mine the set of often occurring subsequences traversal trends in a session database that fulfill a minimal support criterion. Nevertheless, prior frequent sequential traversal pattern mining methods assign a uniform weight to sequential traversal trends, even though the pages inside these trends varied in significance and weight. newlineAnother major issue with the majority of frequently occurring sequential traversal pattern mining techniques is that, when a minimum support is lowered, they produce numerous sequential traversal patterns and refrain from presenting any alternative choices for adjusting the number of sequential traversal patterns besides raising the minimum support. Our proposal for a frequent sequential traversal pattern mining method with dynamic weight constraint is presented in this work. newlineOur major strategy is to preserve the downward closure feature while incorporating the weight limitations into the sequential traversal trend. To preserve the downward closure property, a weight range is established, pages are assigned varying weights, and traversal sequences designate a minimum and maximum weight. newlineA highest and lowest weight in the session database is used to trim rare sequential traversal subsequences during database scanning so that the downward closure attribute may be preserved. By varying the weight range of pages and sequence, our approach generates a few but significant sequential traversal trends in session databases with a low minimum support. newlineFor sequential trends, the most often used metrics are confidence and backing. When sub-trends are provided, the confidence assesses trends frequencies while the support evaluates trends frequencies. For some applications, these factors have significance and importance. To assess the level of surprise of the trends, the newlineinformation gain metric which is frequently employed in the field of information theory might be helpful. The goal is to identify a