AN EFFICIENT ALGORITHM FORMINING HIGH UTILITY ASSOCIATION RULES FROM LATTICE

In business, most of companies focus on growing their profits. Besides considering profit from each product, they also focus on the relationship among products in order to support effective decision making, gain more profits and attract their customers, e.g. shelf arrangement, product displays, or product marketing, etc. Some high utility association rules have been proposed, however, they consume much memory and require long time processing. This paper proposes LHAR (Lattice-based for mining High utility Association Rules) algorithm to mine high utility association rules based on a lattice of high utility itemsets. The LHAR algorithm aims to generates high utility association rules during the process of building lattice of high utility itemsets, and thus it needs less memory and runtim

Efficient algorithms for mining clickstream patterns using pseudo-IDLists

Sequential pattern mining is an important task in data mining. Its subproblem, clickstream pattern mining, is starting to attract more research due to the growth of the Internet and the need to analyze online customer behaviors. To date, only few works are dedicately proposed for the problem of mining clickstream patterns. Although one approach is to use the general algorithms for sequential pattern mining, those algorithms’ performance may suffer and the resources needed are more than would be necessary with a dedicated method for mining clickstreams. In this paper, we present pseudo-IDList, a novel data structure that is more suitable for clickstream pattern mining. Based on this structure, a vertical format algorithm named CUP (Clickstream pattern mining Using Pseudo-IDList) is proposed. Furthermore, we propose a pruning heuristic named DUB (Dynamic intersection Upper Bound) to improve our proposed algorithm. Four real-life clickstream databases are used for the experiments and the results show that our proposed methods are effective and efficient regarding runtimes and memory consumption. © 2020 Elsevier B.V.Vietnam National Foundation for Science and Technology Development (NAFOSTED)National Foundation for Science & Technology Development (NAFOSTED) [02/2019/TN

Efficient Algorithm for Mining Non-Redundant High-Utility Association Rules

In business, managers may use the association information among products to define promotion and competitive strategies. The mining of high-utility association rules (HARs) from high-utility itemsets enables users to select their own weights for rules, based either on the utility or confidence values. This approach also provides more information, which can help managers to make better decisions. Some efficient methods for mining HARs have been developed in recent years. However, in some decision-support systems, users only need to mine a smallest set of HARs for efficient use. Therefore, this paper proposes a method for the efficient mining of non-redundant high-utility association rules (NR-HARs). We first build a semi-lattice of mined high-utility itemsets, and then identify closed and generator itemsets within this. Following this, an efficient algorithm is developed for generating rules from the built lattice. This new approach was verified on different types of datasets to demonstrate that it has a faster runtime and does not require more memory than existing methods. The proposed algorithm can be integrated with a variety of applications and would combine well with external systems, such as the Internet of Things (IoT) and distributed computer systems. Many companies have been applying IoT and such computing systems into their business activities, monitoring data or decision-making. The data can be sent into the system continuously through the IoT or any other information system. Selecting an appropriate and fast approach helps management to visualize customer needs as well as make more timely decisions on business strategy

Herbal Extract Effects on White Spot Syndrome Virus (WSSV) in Shrimp (Penaeus monodon)

Synthetic drugs and chemicals used in aquaculture cause disadvantageous side effects, while medicines made from medicinal herbs are non-toxic, easy to use, and pollution- free. Many medicinal herbs have potent antiviral properties. The extract of Phyllanthus amarus is a lignan composed of the compounds: niranthin, phyllanthin, and hypophyllanthin which have an impact on the white spot syndrome virus (WSSV) in the shrimp, Penaeus monodon. The virucidal activities of the three substances were tested by mixing them with WSSV, followed by injection into healthy shrimp. The quantity of WSSV DNA on the gills of tested shrimp was measured before and seven days after injecting the mixture. The quantity decreased significantly after injection. Anti-virucidal activities were also assessed by observation of the mortality rates of injected shrimp. The lignan compound inactivated the virus when injected in P. monodon at a dose of 100 mg per kilogram body weight. The survival rate of the lignan injected shrimp was 96.67% , compared to the positive control in which it was only 3.33%

Mining clickstream patterns using idlists

To date, there remains a lack of works that focus on the problem of mining clickstream patterns. Although it is an alternative to use the general algorithms for sequential pattern mining to mine clickstreams, their performance may suffer and the resources needed are more than necessary. In this paper, we present a novel data structure, called index-IDList, that is suitable for clickstream pattern mining. Based on this data structure, we present a vertical format algorithm named CUI (Clickstream pattern mining Using Index-IDList). The experiments are carried out on four real-life clickstream databases and the results show that our proposed method is effective and efficient in terms of runtimes and memory consumption. © 2019 IEEE.Vietnam National Foundation for Science and Technology Development (NAFOSTED)National Foundation for Science & Technology Development (NAFOSTED); Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme [LO1303 (MSMT-7778/2014)]; European Regional Development Fund under the Project CEBIA-Tech [CZ.1.05/2.1.00/03.0089]; COST (European Cooperation in Science Technology) [IC1406]; Faculty of Applied Informatics, Tomas Bata University in Zlin (ailab.fai.utb.cz

An efficient method for mining sequential patterns with indices

In recent years, mining informative data and discovering hidden information have become increasingly in demand. One of the popular means to achieve this is sequential pattern mining, which is to find informative patterns stored in databases. Its applications cover different areas and many methods have been proposed. Recently, pseudo-IDLists were proposed to improve both runtime and memory usage in the mining process. However, the idea cannot be directly used for sequential pattern mining as it only works on clickstream patterns, a more distinct type of sequential pattern. We propose adaptations and changes to the original idea to introduce SUI (Sequential pattern mining Using Indices). Comparing SUI with two other state-of-the-art algorithms on six test databases, we show that SUI has effective and efficient performance and memory usage. © 2021 Elsevier B.V.IGA/CebiaTech/2022/00

An efficient parallel algorithm for mining weighted clickstream patterns

In the Internet age, analyzing the behavior of online users can help webstore owners understand customers’ interests. Insights from such analysis can be used to improve both user experience and website design. A prominent task for online behavior analysis is clickstream mining, which consists of identifying customer browsing patterns that reveal how users interact with websites. Recently, this task was extended to consider weights to find more impactful patterns. However, most algorithms for mining weighted clickstream patterns are serial algorithms, which are sequentially executed from the start to the end on one running thread. In real life, data is often very large, and serial algorithms can have long runtimes as they do not fully take advantage of the parallelism capabilities of modern multi-core CPUs. To address this limitation, this paper presents two parallel algorithms named DPCompact-SPADE (Depth load balancing Parallel Compact-SPADE) and APCompact-SPADE (Adaptive Parallel Compact-SPADE) for weighted clickstream pattern mining. Experiments on various datasets show that the proposed parallel algorithm is efficient, and outperforms state-of-the-art serial algorithms in terms of runtime, memory consumption, and scalability. © 2021 Elsevier Inc.Vietnam National Foundation for Science and Technology Development (NAFOSTED)National Foundation for Science & Technology Development (NAFOSTED) [02/2019/TN]National Foundation for Science and Technology Development, NAFOSTED: 02/2019/T

Close-to-ideal spin polarization in zinc-doped Fe–Mo double perovskites at the nanoscale

A high degree of spin polarization in half-metallic double perovskites is a prerequisite for several applications in spintronics, which depends crucially on the cationic order of the systems. This paper reports a study on tailoring the structure and morphology of nano-sized Sr2Fe1-xZnxMoO6 (x = 0.05, 0.1, 0.15) materials to improve their spin polarization. The combined analysis of synchrotron X-ray diffraction and magnetization data shows that Zn replaces Fe in the B sites. Although the majority of particles have lateral dimensions in the range 30–60 nm as observed by scanning electron microscope, the samples with x = 0.1 and 0.15 show finite-size effects with superparamagnetism below room temperature and a reduced Curie temperature (from 410 K for x = 0.05–390 K for x = 0.15). The results are due to the formation of networks of insulating Mo–O–Zn–O–Mo linkages and anti-phase boundaries, which divide the particles into smaller domains with a mean diameter of ∼11 nm as determined via a Langevin fit. The almost perfectly ordered structure in the nanodomains is responsible for a high magnetoresistance ratio. A value of -42% at 5 K in 50 kOe is recorded for the sample x = 0.15. Via fitting the magnetoresistance curve using the Inoue-Mekagawa theory, the spin polarization of 99% is determined

Close-to-ideal spin polarization in zinc-doped Fe–Mo double perovskites at the nanoscale

A high degree of spin polarization in half-metallic double perovskites is a prerequisite for several applications in spintronics, which depends crucially on the cationic order of the systems. This paper reports a study on tailoring the structure and morphology of nano-sized Sr2Fe1-xZnxMoO6 (x = 0.05, 0.1, 0.15) materials to improve their spin polarization. The combined analysis of synchrotron X-ray diffraction and magnetization data shows that Zn replaces Fe in the B sites. Although the majority of particles have lateral dimensions in the range 30–60 nm as observed by scanning electron microscope, the samples with x = 0.1 and 0.15 show finite-size effects with superparamagnetism below room temperature and a reduced Curie temperature (from 410 K for x = 0.05–390 K for x = 0.15). The results are due to the formation of networks of insulating Mo–O–Zn–O–Mo linkages and anti-phase boundaries, which divide the particles into smaller domains with a mean diameter of ∼11 nm as determined via a Langevin fit. The almost perfectly ordered structure in the nanodomains is responsible for a high magnetoresistance ratio. A value of -42% at 5 K in 50 kOe is recorded for the sample x = 0.15. Via fitting the magnetoresistance curve using the Inoue-Mekagawa theory, the spin polarization of 99% is determined