Single-pass incremental and interactive mining for weighted frequent patterns

Weighted frequent pattern (WFP) mining is more practical than frequent pattern mining because it can consider different semantic significance (weight) of the items. For this reason, WFP mining becomes an important research issue in data mining and knowledge discovery. However, existing algorithms cannot be applied for incremental and interactive WFP mining and also for stream data mining because they are based on a static database and require multiple database scans. In this paper, we present two novel tree structures IWFPTWA (Incremental WFP tree based on weight ascending order) and IWFPTFD (Incremental WFP tree based on frequency descending order), and two new algorithms IWFPWA and IWFPFD for incremental and interactive WFP mining using a single database scan. They are effective for incremental and interactive mining to utilize the current tree structure and to use the previous mining results when a database is updated or a minimum support threshold is changed. IWFPWA gets advantage in candidate pattern generation by obtaining the highest weighted item in the bottom of IWFPTWA. IWFPFD ensures that any non-candidate item cannot appear before candidate items in any branch of IWFPTFD and thus speeds up the prefix tree and conditional tree creation time during mining operation. IWFPTFD also achieves the highly compact incremental tree to save memory space. To our knowledge, this is the first research work to perform single-pass incremental and interactive mining for weighted frequent patterns. Extensive performance analyses show that our tree structures and algorithms are very efficient and scalable for single-pass incremental and interactive WFP mining.

► Devising two novel tree structures for efficient weighted frequent pattern mining.
► Development of two new single-scan weighted frequent pattern mining algorithms.
► Description of how to apply our algorithms for incremental and interactive mining.
► Extensive performance study to show the efficiency and effectiveness of our algorithms.