An efficient parallel method for mining frequent closed sequential patterns

Mining frequent closed sequential pattern (FCSPs) has attracted a great deal of research attention, because it is an important task in sequences mining. In recently, many studies have focused on mining frequent closed sequential patterns because, such patterns have proved to be more efficient and compact than frequent sequential patterns. Information can be fully extracted from frequent closed sequential patterns. In this paper, we propose an efficient parallel approach called parallel dynamic bit vector frequent closed sequential patterns (pDBV-FCSP) using multi-core processor architecture for mining FCSPs from large databases. The pDBV-FCSP divides the search space to reduce the required storage space and performs closure checking of prefix sequences early to reduce execution time for mining frequent closed sequential patterns. This approach overcomes the problems of parallel mining such as overhead of communication, synchronization, and data replication. It also solves the load balance issues of the workload between the processors with a dynamic mechanism that re-distributes the work, when some processes are out of work to minimize the idle CPU time.Web of Science5174021739

Time Series Trend Analysis Based on K-Means and Support Vector Machine

In this paper, we apply both supervised and unsupervised machine learning techniques to predict the trend of financial time series based on trading rules. These techniques are K-means for clustering the similar group of data and support vector machine for training and testing historical data to perform a one-day-ahead trend prediction. To evaluate the method, we compare the proposed method with traditional back-propagation neural network and a standalone support vector machine. In addition, to implement this combination method, we use the financial time series data obtained from Yahoo Finance website and the experimental results also validate the effectiveness of the method

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

Improving Efficiency of Incremental Mining by Trie Structure and Pre-Large Itemsets

Incremental data mining has been discussed widely in recent years, as it has many practical applications, and various incremental mining algorithms have been proposed. Hong et al. proposed an efficient incremental mining algorithm for handling newly inserted transactions by using the concept of pre-large itemsets. The algorithm aimed to reduce the need to rescan the original database and also cut maintenance costs. Recently, Lin et al. proposed the Pre-FUFP algorithm to handle new transactions more efficiently, and make it easier to update the FP-tree. However, frequent itemsets must be mined from the FP-growth algorithm. In this paper, we propose a Pre-FUT algorithm (Fast-Update algorithm using the Trie data structure and the concept of pre-large itemsets), which not only builds and updates the trie structure when new transactions are inserted, but also mines all the frequent itemsets easily from the tree. Experimental results show the good performance of the proposed algorithm

A graph-based CNN-LSTM stock price prediction algorithm with leading indicators

In today's society, investment wealth management has become a mainstream of the contemporary era. Investment wealth management refers to the use of funds by investors to arrange funds reasonably, for example, savings, bank financial products, bonds, stocks, commodity spots, real estate, gold, art, and many others. Wealth management tools manage and assign families, individuals, enterprises, and institutions to achieve the purpose of increasing and maintaining value to accelerate asset growth. Among them, in investment and financial management, people's favorite product of investment often stocks, because the stock market has great advantages and charm, especially compared with other investment methods. More and more scholars have developed methods of prediction from multiple angles for the stock market. According to the feature of financial time series and the task of price prediction, this article proposes a new framework structure to achieve a more accurate prediction of the stock price, which combines Convolution Neural Network (CNN) and Long-Short-Term Memory Neural Network (LSTM). This new method is aptly named stock sequence array convolutional LSTM (SACLSTM). It constructs a sequence array of historical data and its leading indicators (options and futures), and uses the array as the input image of the CNN framework, and extracts certain feature vectors through the convolutional layer and the layer of pooling, and as the input vector of LSTM, and takes ten stocks in U.S.A and Taiwan as the experimental data. Compared with previous methods, the prediction performance of the proposed algorithm in this article leads to better results when compared directly

Assessing the relationship between work-related factors and the quality of working life among nurses: A cross-sectional study in Laos

The quality of working life is crucial for improving work productivity, particularly among nurses, who often experience high levels of stress. This study aims to evaluate the quality of working life among nurses in Laos and identify the factors that influence it. A cross-sectional study was conducted among Laos nurses from August 2021 to July 2022. Data collection was conducted using an anonymous questionnaire distributed via the Internet. The Quality of Working Life version 2 (WRQoL-2) questionnaire, comprising 32 items divided into seven subscales, was employed to assess the quality of working life. Statistical tests such as t-tests, ANOVA, and Spearman correlation were applied to examine differences and correlations. A total of 326 participants were included, with an average age of 32.62±8.21 years. Among the seven subscales, the highest score was observed in the Job Career Satisfaction subscale (3.72±0.56), while the lowest score was found in the Safety at Work subscale (3.22±0.71). The overall mean score was 3.49±0.54. Significant differences in the quality of working life were observed among different groups categorized by age, job position, salary, and working hours. The WRQoL-2 questionnaire was found to be suitable for assessing the quality of working life in this study