Abstract
Incremental mining of frequent patterns has attracted the attention of researchers in the last two decades. The researchers have explored the frequent pattern mining from incremental database problems by considering that the complete database to be processed can be accommodated in systems’ main memory even after the database gets updated very frequently. The FP-tree-based approaches were able to draw more interest because of their compact representation and requirement of a minimum number of database scans. The researchers have developed a few FP-tree based methods to handle the incremental scenario by adjusting or restructuring the tree prefix paths. Although the approaches have managed to solve the re-computation problem by constructing a complete pattern tree data structure using only one database scan, restructuring the prefix paths for each transaction is a computationally costly task, leading to the high tree construction time. If the FP-tree construction process can be supported with suitable data structures, reconstruction of the FP-tree from scratch may be less time consuming than the restructuring approaches in case of incremental scenario. In this study, we have proposed a tree data structure called Improved Frequent Pattern tree (Improved FP-tree). The proposed Improved FP-tree construction algorithm has immensely improved the performance of tree construction time by resourcefully using node links, maintained in header table to manage the same item node list in the FP-tree. The experimental results emphasize the significance of the proposed Improved FP-tree construction algorithm over a few conventional incremental FP-tree construction algorithms with prefix path restructuring.
Highlights
In this 21st century, transactional databases are dynamic
The performance enhancement is achieved by maintaining each list of nodes containing the same item of Improved Frequent Pattern tree (FP-tree) as a linked-list implemented as stack instead of maintaining a simple linked-list as a conventional FP-tree
The FP-tree-based incremental frequent pattern mining approaches perform frequent pattern mining by considering that the complete database to be processed can be accommodated in the systems main memory even after the database gets updated very frequently
Summary
Researchers have focused on finding the hidden knowledge from these incremental databases. Frequent pattern mining is one of the most widely used knowledge retrieval techniques of data mining. Apriori algorithm is a level-wise computation, which employs multiple database scans and generates an enormous number of candidate itemsets. It exercises costly testing and prune out approach to discard the redundant and infrequent candidate itemsets to generate the complete set of frequent patterns. Many attempts have been made by the researchers to propose an efficient method to mine the frequent itemsets from large datasets by adopting the Apriori approach. Most of the proposed approaches sustain the same multiple database scans, computation time (candidate itemsets) and space problems
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
