Advancements in the field of information technology have resulted in an increase in the speed and amount of data generated. This has resulted in traditional association rules algorithms, such as the Apriori and Frequent Pattern Growth (FPGrowth) algorithms, no longer being able to rapidly explore valuable knowledge in big data. Nowadays, parallel computing with technologies such as MapReduce is commonly used to reduce execution times. Because the FP-Growth algorithm uses an FPTree to mine itemsets, decomposing its structure into subtasks is difficult . We developed a method that combines the Apriori and FP-Growth algorithms with MapReduce to rectify this problem. In experiments conducted, we varied the block size of the Mapper to achieve execution performance better than those of the Apriori and FP-Growth algorithms.
This paper applies a two-stage banking model to analyze the operational efficiency of 137 Asian banks. Tobit regression model is also used to investigate the effect under the different operating environment and the characteristics of banks on banking efficiency. The empirical results show that technical inefficiency in the production stage for all the Asian banks is caused by pure technical inefficiency. In the intermediary stage, the banks' technical inefficiencies in China, Taiwan, and South Korea are mainly caused by pure technical inefficiency, whereas in Hong Kong, Malaysia, Thailand, Singapore and Philippines, it is caused by scale inefficiency. From the policy perspective, this study can help Asian banks
Abstract:In recent years, wireless charging technology has provided an alternative to charging equipment. Wireless charging technology has already proved to be useful in our daily lives in phones, buses, restaurants, etc. Wireless charging technology can also be applied in energy-bounded wireless sensor networks (WSNs), and these are called wireless rechargeable sensor networks (WRSNs). The optimized charging path problem is the most widely discussed issue in employing WRSNs with wireless charging vehicles (WCVs). This problem involves determining the most efficient path for charging sensor nodes. Further, charging-scheduling problems also need to be considered in the optimized charging path problem. In this paper, we proposed a multi-module charging strategy (MMCS) used to prolong the lifetime of the entire WRSN. MMCS can be divided into three stages: the charging topology, charging scheduling, and charging strategy stages, with multiple modules in each stage. The best module combination of MMCS is the distance-based module in the charging topology stage, delay-based module in the charging schedule stage, and the average lifetime module in the charging strategy stage. The best module combination enables prolonging the lifetime efficiently, as it considers not only the priority of urgent nodes but also the travel distance of WCV; the delay-based module of the charging schedule stage considers the delay effect on the follow-up nodes. The experimental results show that the proposed MMCS can improve the lifetime of the entire WRSN and that it substantially outperforms the nearest job next with preemption (NJNP) method in terms of lifetime improvement of the entire WRSN.
In this paper, a cluster validity index called CDV index is presented. The CDV index is capable of providing a quality measurement for the goodness of a clustering result for a data set. The CDV index is composed of three major factors, including a statistically calculated external diameter factor, a restorer factor to reduce the effect of data dimension, and a number of clusters related punishment factor. With the calculation of the product of the three factors under various number of clusters settings, the best clustering result for some number of clusters setting is able to be found by searching for the minimum value of CDV curve. In the empirical experiments presented in this research, K-Means clustering method is chosen for its simplicity and execution speed. For the presentation of the effectiveness and superiority of the CDV index in the experiments, several traditional cluster validity indexes were implemented as the control group of experiments, including DI, DBI, ADI, and the most effective PBM index in recent years. The data sets of the experiments are also carefully selected to justify the generalization of CDV index, including three real world data sets and three artificial data sets which are the simulation of real world data distribution. These data sets are all tested to present the superior features of CDV index.
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