Dual incremental fuzzy schemes for frequent itemsets discovery in streaming numeric data

Zheng, Hui; Li, Peng; Liu, Qing; Chen, Jinjun; Huang, Guang-Li; Wu, Junfeng; Xue, Yun; He, Jing

doi:10.1016/j.ins.2019.11.023

Cited by 4 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At the same time, to improve the accuracy of neighbor selection, the model also proposes a novel similarity calculation method. Zheng et al 37 proposes the two incremental schemes for discovering frequent item-sets, which can effectively process streaming numeric data and provide a novel idea for service recommendation using association rules. Lin et al 38 proposed data quality-based user recruitment in mobile crowd sensing, and explained that recruiting users who can provide high-quality data can ensure the success of the task.…”

Section: Web Service Recommendation Algorithmsmentioning

confidence: 99%

DVO + LCLMF: A web service recommendation mechanism with QoS privacy preservation

Liu

et al. 2022

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

QoS‐aware based web service recommendation is one of the crucial solutions to help users find high‐quality web services. To accurately predict the QoS values of candidate services, it is usually required to collect historical QoS data of users (QoS data for short). If these collected QoS data are improperly processed, QoS data privacy may be threatened. However, how to accurately predict the QoS values of candidate services while protecting QoS data privacy has not been well studied. In response to the situation, we propose a hybrid web service recommendation mechanism, which is divided into three parts. In the first part, the QoS data privacy preservation algorithm, which called DVO, is proposed based on keeping the cosine similarity of QoS data unchanged, that is, to realize the confusion of QoS data while ensuring the availability of QoS data remains unchanged. In the second part, a hybrid matrix factorization model based on location information and service features, which called LCLMF, is proposed to improve the accuracy of QoS values prediction. According to DVO and LCLMF, the DVO + LCLMF is designed in the third part, which can accurately predict QoS values while protecting QoS data privacy. The experimental results show that DVO + LCLMF can accurately predict the QoS values of candidate services on the basis of attaining QoS data privacy protection.

show abstract

Section: Web Service Recommendation Algorithmsmentioning

confidence: 99%

DVO + LCLMF: A web service recommendation mechanism with QoS privacy preservation

Liu

et al. 2022

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

show abstract

A Novel Association Rule Mining Method for Streaming Temporal Data

Zheng

2021

Ann. Data. Sci.

View full text Add to dashboard Cite

A Synopsis Based Approach for Itemset Frequency Estimation over Massive Multi-Transaction Stream

Wang

Cong

Zhang

et al. 2021

ACM Trans. Knowl. Discov. Data

View full text Add to dashboard Cite

The streams where multiple transactions are associated with the same key are prevalent in practice, e.g., a customer has multiple shopping records arriving at different time. Itemset frequency estimation on such streams is very challenging since sampling based methods, such as the popularly used reservoir sampling, cannot be used. In this article, we propose a novel k -Minimum Value (KMV) synopsis based method to estimate the frequency of itemsets over multi-transaction streams. First, we extract the KMV synopses for each item from the stream. Then, we propose a novel estimator to estimate the frequency of an itemset over the KMV synopses. Comparing to the existing estimator, our method is not only more accurate and efficient to calculate but also follows the downward-closure property. These properties enable the incorporation of our new estimator with existing frequent itemset mining (FIM) algorithm (e.g., FP-Growth) to mine frequent itemsets over multi-transaction streams. To demonstrate this, we implement a KMV synopsis based FIM algorithm by integrating our estimator into existing FIM algorithms, and we prove it is capable of guaranteeing the accuracy of FIM with a bounded size of KMV synopsis. Experimental results on massive streams show our estimator can significantly improve on the accuracy for both estimating itemset frequency and FIM compared to the existing estimators.

show abstract

Dual incremental fuzzy schemes for frequent itemsets discovery in streaming numeric data

Cited by 4 publications

References 16 publications

DVO + LCLMF: A web service recommendation mechanism with QoS privacy preservation

DVO + LCLMF: A web service recommendation mechanism with QoS privacy preservation

A Novel Association Rule Mining Method for Streaming Temporal Data

A Synopsis Based Approach for Itemset Frequency Estimation over Massive Multi-Transaction Stream

Contact Info

Product

Resources

About