Data mining in IoT era:A method based on improved frequent items mining algorithm

Wang, Zhao; Liang, Wenqian; Zhang, Yaohong; Wang, Ji; Tao, Jinjin; Chen, Chao; Yan, Hui; Men, Tong

doi:10.1109/bigdia.2019.8802727

Cited by 7 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the experimental results were not extensively analysed for the proposed method. In [83], an improved version of frequent pattern mining technique has been proposed that targeted IoT big data. They modified the Apriori algorithm as an advanced Apriori algorithm (ADAA) to identify an item's association where the ADAA is less sensitive to the minimum support threshold compared to the traditional Apriori algorithm.…”

Section: B Association Rule Based Bps Mining Techniquesmentioning

confidence: 99%

A Survey on Behavioral Pattern Mining From Sensor Data in Internet of Things

et al. 2020

View full text Add to dashboard Cite

The deployment of large-scale wireless sensor networks (WSNs) for the Internet of Things (IoT) applications is increasing day-by-day, especially with the emergence of smart city services. The sensor data streams generated from these applications are largely dynamic, heterogeneous, and often geographically distributed over large areas. For high-value use in business, industry and services, these data streams must be mined to extract insightful knowledge, such as about monitoring (e.g., discovering certain behaviors over a deployed area) or network diagnostics (e.g., predicting faulty sensor nodes). However, due to the inherent constraints of sensor networks and application requirements, traditional data mining techniques cannot be directly used to mine IoT data streams efficiently and accurately in real-time. In the last decade, a number of works have been reported in the literature proposing behavioral pattern mining algorithms for sensor networks. This paper presents the technical challenges that need to be considered for mining sensor data. It then provides a thorough review of the mining techniques proposed in the recent literature to mine behavioral patterns from sensor data in IoT, and their characteristics and differences are highlighted and compared. We also propose a behavioral pattern mining framework for IoT and discuss possible future research directions in this area. INDEX TERMS Association rules, behavioral patterns, data mining, frequent pattern, Internet of Things, knowledge discovery, wireless sensor networks.

show abstract

Section: B Association Rule Based Bps Mining Techniquesmentioning

confidence: 99%

A Survey on Behavioral Pattern Mining From Sensor Data in Internet of Things

et al. 2020

View full text Add to dashboard Cite

show abstract

“…After preprocessing this coarse-grained data, some artificial intelligence method can be utilized for effective analysis, such as to study customer buying patterns, effective catalog deign, efficient stock management, etc. [ 9 , 10 , 11 ]. Frequent itemsets mining is one such method in association rule mining that can serve the aforementioned purpose perfectly.…”

Section: Introductionmentioning

confidence: 99%

Efficient Top-K Identical Frequent Itemsets Mining without Support Threshold Parameter from Transactional Datasets Produced by IoT-Based Smart Shopping Carts

Rehman

Alnazzawi

Ashraf

et al. 2022

Sensors

View full text Add to dashboard Cite

Internet of Things (IoT)-backed smart shopping carts are generating an extensive amount of data in shopping markets around the world. This data can be cleaned and utilized for setting business goals and strategies. Artificial intelligence (AI) methods are used to efficiently extract meaningful patterns or insights from such huge amounts of data or big data. One such technique is Association Rule Mining (ARM) which is used to extract strategic information from the data. The crucial step in ARM is Frequent Itemsets Mining (FIM) followed by association rule generation. The FIM process starts by tuning the support threshold parameter from the user to produce the number of required frequent patterns. To perform the FIM process, the user applies hit and trial methods to rerun the aforesaid routine in order to receive the required number of patterns. The research community has shifted its focus towards the development of top-K most frequent patterns not using the support threshold parameter tuned by the user. Top-K most frequent patterns mining is considered a harder task than user-tuned support-threshold-based FIM. One of the reasons why top-K most frequent patterns mining techniques are computationally intensive is the fact that they produce a large number of candidate itemsets. These methods also do not use any explicit pruning mechanism apart from the internally auto-maintained support threshold parameter. Therefore, we propose an efficient TKIFIs Miner algorithm that uses depth-first search strategy for top-K identical frequent patterns mining. The TKIFIs Miner uses specialized one- and two-itemsets-based pruning techniques for topmost patterns mining. Comparative analysis is performed on special benchmark datasets, for example, Retail with 16,469 items, T40I10D100K and T10I4D100K with 1000 items each, etc. The evaluation results have proven that the TKIFIs Miner is at the top of the line, compared to recently available topmost patterns mining methods not using the support threshold parameter.

show abstract

“…The proposed method will greatly promote 5G-based individualized and corresponding emotion-aware medical services [12]. With the development and popularization of 5G technology and IoT, new data mining algorithms were boosted [13]. Big data, IoT, and 5G are closely related to each other, for big data stems from the application of IoT technology.…”

Section: Introductionmentioning

confidence: 99%

Stock Forecasting Model FS-LSTM Based on the 5G Internet of Things

Hua

et al. 2020

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

This paper analyzed the development of data mining and the development of the fifth generation (5G) for the Internet of Things (IoT) and uses a deep learning method for stock forecasting. In order to solve the problems such as low accuracy and training complexity caused by complicated data in stock model forecasting, we proposed a forecasting method based on the feature selection (FS) and Long Short-Term Memory (LSTM) algorithm to predict the closing price of stock. Considering its future potential application, this paper takes 4 stock data from the Shenzhen Component Index as an example and constructs the feature set for prediction based on 17 technical indexes which are commonly used in stock market. The optimal feature set is decided via FS to reduce the dimension of data and the training complexity. The LSTM algorithm is used to forecast closing price of stock. The empirical results show that compared with the LSTM model, the FS-LSTM combination model improves the accuracy of prediction and reduces the error between the real value and the forecast value in stock price prediction.

show abstract

Data mining in IoT era:A method based on improved frequent items mining algorithm

Cited by 7 publications

References 12 publications

A Survey on Behavioral Pattern Mining From Sensor Data in Internet of Things

A Survey on Behavioral Pattern Mining From Sensor Data in Internet of Things

Efficient Top-K Identical Frequent Itemsets Mining without Support Threshold Parameter from Transactional Datasets Produced by IoT-Based Smart Shopping Carts

Stock Forecasting Model FS-LSTM Based on the 5G Internet of Things

Contact Info

Product

Resources

About