Abstract: 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 incre- mental 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 operation1.
Title: Single-Pass Incremental and Interactive Mining for Weighted Frequent Pattern
Author: Dayanand Sunil Sonavane, Dayanand Suresh Chilap, Parag Jalindar Davkhar, Mayur Gulab Varpe
International Journal of Computer Science and Information Technology Research
ISSN 2348-120X (online), ISSN 2348-1196 (print)
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