Fuzzy association rule mining approach to identify e-commerce product association considering sales amount

Online stores assist customers in buying the desired products online. Great competition in the e-commerce sector necessitates technology development. Many e-commerce systems not only present products but also offer similar products to increase online customer interest. Due to high product variety, analyzing products sold together similar to a recommendation system is a must. This study methodologically improves the traditional association rule mining (ARM) method by adding fuzzy set theory. Besides, it extends the ARM by considering not only items sold but also sales amounts. Fuzzy association rule mining (FARM) with the Apriori algorithm can catch the customers’ choice from historical transaction data. It discovers fuzzy association rules from an e-commerce company to display similar products to customers according to their needs in amount. The experimental result shows that the proposed FARM approach produces much information about e-commerce sales for decision-makers. Furthermore, the FARM method eliminates some traditional rules considering their sales amount and can produce some rules different from ARM.

Solar-powered IoT based smart hydroponic nutrition management system using FARM

Good nutrition and water conditions are significant in a hydroponic system. Nutrients in hydroponic systems are periodically re-mixed to target the right amount of TDS. The TDS amount needs to be regularly measured after the weather changes, namely temperature, humidity, light intensity, and rainfall. The research proposes developing a solar-powered Internet of Things (IoT) based Smart Hydroponic Nutrition Management System using Fuzzy Association Rule Mining (FARM). The system consists of an IoT connected to a TDS (Total Dissolved Solids) sensor, a relay module connected to two 5v mini pumps that supply AB Mix nutrients, and a solenoid valve to supply water. The IoT system is also connected to sensors for temperature and humidity, light intensity, and rainfall to record the causes of weather changes that cause changes in TDS in hydroponic water. FARM is used to extract fuzzy association rules (FAR) from IoT sensors. The system targets a TDS of 1200 for leafy plants such as lettuce. The system prototype was developed in a small 5×7cm single layer PCB using the wire-wrapping technique. The test results produce a standard deviation of 2.345 for the TDS average of 1196.17 and threshold 50. In one week of evaluation, three times of rain and four times of hot weather were considered to change the TDS, and seven actions of the relay module were carried out. FARM has extracted fuzzy rules with average support of 0.401 and confidence of 0.826.

Research on Information Security Risk Assessment Method Based on Fuzzy Rule Set

With the increasing complexity of the network structure and the increasing size of the network, various network security incidents pose an increasing threat to the security of computer systems and the network. Especially, in the network environment, the diversified intrusion methods and application environment make the security of the network more fragile. In order to improve information security, based on fuzzy rule sets, this paper proposes a fuzzy association rule mining algorithm based on fuzzy matrix and applies it to security event correlation. In addition, this paper combines the embedded system to construct an information security risk assessment system and sets the system performance based on the actual situation. Finally, this paper carries out experimental design to verify the performance of the system and analyzes the experimental results by mathematical statistics. From the experimental research, it can be seen that the system constructed in this paper has a certain effect.

Subset-binding: A novel algorithm to detect paired itemsets from heterogeneous data including biological datasets

The integration of heterogeneous data to infer latent relationships across them and find the factors in the relationship is a challenging task. In this regard, various machine learning techniques have provided novel insights through data integration. However, concerns remain regarding their application to biological datasets because the latent consensus information across all views is often limited to partial components that do not have a significant impact on the mutual agreement across views. Advocating the idea of “subset-binding,” which focuses on finding inter-related attributes in heterogeneous data according to their co-occurrence, this study developed a novel algorithm to perform subset-binding by extending fuzzy association rule mining techniques. Our method could detect genes related to liver toxicity caused by acetaminophen in a data-driven manner; the results are consistent with those reported in the literature. This technology paves the way for a wide range of applications, including biomarker detection and patient stratification.