Extensive efforts have been devoted to developing efficient algorithms for mining frequent patterns. However, frequent pattern mining remains a time-consuming process, especially for very large datasets. It is therefore desirable to adopt a "mining once and using many times" strategy. Unfortunately, there has been little work reported on managing and organizing a large set of patterns for future use. In this paper, we propose a disk-based data structure, CFP-tree (Condensed Frequent Pattern Tree), for organizing frequent patterns discovered from transactional databases. In addition to an efficient algorithm for CFP-tree construction, we also developed algorithms to efficiently support two important types of queries, namely queries with minimum support constraints and queries with item constraints, against the stored patterns, as these two types of queries are basic building blocks for complex frequent pattern related mining tasks. Comprehensive experimental study has been conducted to demonstrate the effectiveness of CFP-tree and efficiency of related algorithms.

Web access prediction is an active research topic with many applications. Various approaches have been proposed for Web access prediction in the domain of individual Web servers but they have to be tailored to the domain of proxy servers to satisfy its special requirements in prediction efficiency and scalability. In this paper, the design and implementation of proxy-based prediction service (PPS) is presented. For prediction efficiency, PPS applies a new prediction scheme which employs a two-layer navigation model to capture both inter-site and intra-site access patterns, incorporated with a bottom-up prediction mechanism that exploits reference locality in proxy logs. For system scalability, PPS manages the navigation model in disk database and adopts a predictive cache replacement strategy for data shipping between the model database and cache. We show the superiority of our prediction scheme over existing approaches and validate our model management and caching strategies, with a detailed performance study using real-world data.