A Novel Approach for Web Page Set Mining

TL;DR

This paper introduces a hash index table structure for efficient web page set mining from server log files, enabling faster processing, incremental updates, and better performance compared to traditional flat file algorithms.

Contribution

It presents a novel hash index table method for web page set mining that improves efficiency and supports incremental updates without reaccessing the original database.

Findings

Performance is comparable or better than flat file algorithms.

Supports incremental updates without reprocessing the entire database.

Effective for both sparse and dense data distributions.

Abstract

The one of the most time consuming steps for association rule mining is the computation of the frequency of the occurrences of itemsets in the database. The hash table index approach converts a transaction database to an hash index tree by scanning the transaction database only once. Whenever user requests for any Uniform Resource Locator (URL), the request entry is stored in the Log File of the server. This paper presents the hash index table structure, a general and dense structure which provides web page set extraction from Log File of server. This hash table provides information about the original database. Web Page set mining (WPs-Mine) provides a complete representation of the original database. This approach works well for both sparse and dense data distributions. Web page set mining supported by hash table index shows the performance always comparable with and often better than algorithms accessing data on flat files. Incremental update is feasible without reaccessing the original transactional database.