An efficient algorithm for mining frequent weighted itemsets using interval word segments

Nguyen, Ham; Vo, Bay; Nguyen, Minh Hieu; Pedrycz, Witold

doi:10.1007/s10489-016-0799-6

Cited by 16 publications

(1 citation statement)

References 23 publications

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“…The authors [4] developed some theorems to rapidly calculate weighted supports, giving WIT-FWI-DIFF impressive results in their experiments. After that, Nguyen et al [5] utilized the IWS structure to optimize the tidsets storage space for accelerating the computation of mining FWPs. Based on this structure, the IWS algorithm was introduced.…”

Section: A Mining Frequent Weighted Patternsmentioning

confidence: 99%

A Sliding Window-Based Approach for Mining Frequent Weighted Patterns Over Data Streams

Bui¹,

Nguyen-Hoang²,

et al. 2021

IEEE Access

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The mining of frequent weighted patterns (FWPs) that considers the different semantic significance (weight) of items is more suitable for practice than the mining of frequent patterns. Therefore, it plays a vital role in real-world scenarios. However, there exist several limitations when applying methods for mining FWPs designed for static data on growth datasets, especially data streams. Hence, this study proposes an algorithm for mining FWPs over data streams. First, we introduce the concept of mining FWPs over data streams via a sliding window model. Then, we introduce a modification of the weighted node tree (WN-tree) named SWN-tree that has the ability to maintain the information over data streams. Next, this study develops a method for mining FWPs over data streams employing a sliding window model based on SWN-tree. This method is called FWPODS (Frequent Weighted Patterns Over Data Stream) algorithm. Finally, we conduct empirical experiments to compare the performances of our approach and the state-of-the-art algorithm (NFWI) for mining FWPs over data streams. The results of experiment indicate that our approach outperforms the NFWI algorithm when running in batch mode in a sliding window.INDEX TERMS pattern mining, data streams, frequent weighted patterns, sliding window model.

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Section: A Mining Frequent Weighted Patternsmentioning

confidence: 99%