Mining and Evaluation of Teaching Civics Courses Based on Apriori-TIDS Algorithm

The thinking course is an innovative move to implement the fundamental task of moral education and realize the whole process of education and all-round education in universities. The Apriori-TIDS algorithm proposed in this paper adopts the TID list of transaction identifiers to calculate the support count and generate the frequent item set of the Hou option set, and the whole frequency set generation process only needs to scan the transaction database once, which greatly improves the operation efficiency of the mining algorithm. The course is based on the three focus points of ideological and political education, such as “matters, times, and situations”, to explore the elements of ideological and political education hidden in the course, and to give the principles and criteria for evaluating the effectiveness of ideological and political teaching in the course, in order to make the professional degree course become the main channel to lead the ideological and political education of postgraduate courses and improve the effectiveness of the course in educating people.

An efficient HUIBA Algorithm for Mining High Utility Itemset

Utility Mining is to spot the itemsets with highest utilities, by considering profit, quantity, cost or other user preferences. Mining High Utility itemsets from a transaction database is to seek out itemsets that have utility above a user-specified threshold. Bio inspired algorithm is extremely efficient for mining High Utility Itemset(HUI), but it will not find all HUI in the database and the quality is poor within the number of discovered HUI. A replacement framework using BA algorithm is proposed to rectify this issue. The proposed algorithm is more efficient in terms of quality and convergence speed when put next to other algorithms.

Analysis of Rural Tourism Demand Characteristics and Experience Differences Based on Association Rule Mining

Whether the characteristics of rural tourism changes or not provides the scale and basis for judging whether the rural tourism landscape has changed, but it cannot provide a judgment on the impact of rural tourism landscape changes. The impact is relative to the rural tourism landscape goal. The determination of rural tourism landscape objectives provides a baseline for judging the direction and impact of rural tourism characteristics and provides a prerequisite for rural tourism landscape actions. The determination of the quality target of the rural tourism landscape is mainly determined by the internal process and external demand of the rural tourism landscape. Through in-depth research on the frequent pattern growth algorithm FP-Growth, the algorithm can find frequent item sets by not generating candidate item sets. The core of the algorithm is the frequent pattern tree FP-tree, which can efficiently compress the transaction database. Based on the advantages of FP-tree, this paper improves a FP_Apriori algorithm based on frequent pattern trees. This algorithm projects the entire transaction database onto the FP-tree, avoiding a lot of I/O overhead. At the same time, I propose a more directional and targeted search strategy for FP-tree, which reduces the running time of the algorithm and uses the principle of the Mapping_Apriori algorithm to prethin the frequent item sets. This article uses the text analysis method of network data to excavate the characteristics and internal structure of rural tourism demand. The rural tourism market has a wide range of needs and multiple levels, and traditional research methods such as questionnaires have limited sample size and sample structure. With the help of network data, text mining, and other statistical analysis methods, in-depth empirical research on the characteristics and spatial structure of rural tourism in a certain region can cover more research groups. The research confirms that the results of using text analysis and questionnaire analysis on the perception of destination image are relatively consistent. Therefore, the network text analysis method is an effective tool to study the demand structure of the rural tourism market.

Souvenir Sales Analysis using Apriori Algorithm (Case Study: Ubud-Market Transaction in March 2020)

Ubud market is one of the largest art markets in Bali, there are many local Balinese souvenir traders and craftspeople, most of them are livelihoods depend on buying and selling local souvenirs, Since the Covid-19 pandemic entered in April 2020, Ubud market traders have started to close their business and hoping economic recoveryin future. The author tries to do a track record of souvenir sales transactions in Ubud market to find the last sales pattern before the traders closes their business to give a solution for marketing strategies in future. The sales transaction data will just become meaningless trash if it’s useless.. To get use information about the products that are most sold out at Ubud Market from the transaction database, the author uses the Apriori algorithm. This study was determined final rules on 2 itemset combination, If buying Manik-Manik Craft, Also buy Barong Shirt with the highest confidence 70% and Minimum Support 28%, and for 3 itemset a combination, If buying Celuk Silver, and Barong Shirt, Also buy Manik-Manik Craft with the highest confidence 37.5% and Minimum Support 12%, based on that there are 3 best-selling souvenir products, namely Barong Shirt, Manik-Manik Craft and Silver-Celuk in March 2020. Keywords: Apriori Algorithm, Data Mining, Sales Analysis, Association Rule Mining, Ubud Market.

Exploring Decomposition for Solving Pattern Mining Problems

This article introduces a highly efficient pattern mining technique called Clustering-based Pattern Mining (CBPM). This technique discovers relevant patterns by studying the correlation between transactions in the transaction database based on clustering techniques. The set of transactions is first clustered, such that highly correlated transactions are grouped together. Next, we derive the relevant patterns by applying a pattern mining algorithm to each cluster. We present two different pattern mining algorithms, one applying an approximation-based strategy and another based on an exact strategy. The approximation-based strategy takes into account only the clusters, whereas the exact strategy takes into account both clusters and shared items between clusters. To boost the performance of the CBPM, a GPU-based implementation is investigated. To evaluate the CBPM framework, we perform extensive experiments on several pattern mining problems. The results from the experimental evaluation show that the CBPM provides a reduction in both the runtime and memory usage. Also, CBPM based on the approximate strategy provides good accuracy, demonstrating its effectiveness and feasibility. Our GPU implementation achieves significant speedup of up to 552× on a single GPU using big transaction databases.

THE ALGORITHM FOR MARKING VERTEXES. COMPARISON OF ALGORITHMS FOR SEARCHING FOR CONNECTIVITY COMPONENTS IN A GRAPH USING THE EXAMPLE OF COMBINING WALLETS IN A BITCOIN DATABASE

The article is devoted to comparing the efficiency of algorithms for processing Bitcoin blockchain transaction database. The article describes the algorithm of vertex marking developed by the group. Based on the comparison of this and other algorithms, it is expected to identify the most effective algorithm for clustering addresses based on belonging to a single user. The Bitcoin database contains information about millions of financial transactions. Even though information about transactions is anonymous, there are methods for combining user addresses into wallets. In this article, we study algorithms of searching connectivity components, which are based on one of the methods of combining wallets based on the heuristic feature of the «total waste» of one user. The emphasis is placed on the practical aspects of implementation – hardware limitations in processing big data sets, as well as the choice of a solution for many graph connectivity components – the maximum connected set of graph vertices, in other words, a set of nonempty vertex sets and a set of vertex pairs.