연관성 규칙 수의 추정을 위한 일반적인 비선형 회귀모형에서의 표준화 향상도 활용 방안

Mining association rules in large databases is one of the most interesting data mining techniques in database communities. The number of association rules in a large database highly depends on rule’s support and confidence, and there might exist hundreds of rules in a very large database. The presentation of large number of rules in a very nice and noticeable way becomes highly challenging. In recent years researchers have developed several tools to visualize association rules. However, a large number of the tools cannot handle more than dozens of association rules. Furthermore, none of them can effectively manage association rules with multiple antecedents. Till now a uniform descriptive presentation technique has not been set up yet. We studied existing descriptive techniques in the context of visualization and introduced a graph-based technique as Unified Descriptive Language for Association Rules. The proposed technique can be used to extract the discovered rules of a very large database very conveniently and efficiently.

Discovering association rules among items in large databases in recognized as an important database mining problem. The problem has been introduced originally for sales transaction database and did not relate to missing data. However, missing data often occur in relational databases, especially in business ones. It is not obvious how to compute association rules from such incomplete databases. It is provided and proved in the paper how to estimate support and confidence of an association rule induced from an incomplete relational database. We also introduce definitions of expected support and confidence of an association rule. The proposed definitions guarantee some required properties of itemsets and association rules. Eventually, we discuss another approach to missing values based on so called valid databases and compare both approaches.

Data mining technology has emerged as a means for identifying patterns and trends from large quantities of data. Mining encompasses various algorithms such as clustering, classification, and association rule mining. In this paper we take advantage of the genetic algorithm (GA) designed specifically for discovering association rules. We propose a novel spatial mining algorithm, called ARMNGA(association rules mining in novel genetic algorithm), Compared to the algorithm in [Agrawal R, et al., Mining association rules between sets of items in large databases, 1993], the ARMNGA algorithm avoids generating impossible candidates, and therefore is more efficient in terms of the execution time.

Data mining involves the use of advanced data analysis tools to find out new, suitable patterns and project the relationship among the patterns which were not known prior. In data mining, association rule learning is a popular and well researched method for discovering interesting relations between variables in large databases. One of the emerging research areas under Data mining is Social Networks. The objective of this paper focuses on the formulation of association rules using which decisions can be made for future Endeavour. This research applies Apriori Algorithm which is one of the classical algorithms for deriving association rules. The Algorithm is applied to Face book 100 university dataset which has originated from Adam D’Angelo of Face book. It contains self-defined characteristics of a person including variables like residence, year, and major, second major, gender, school. This paper to begin with the research uses only ten Universities and highlights the formation of association rules between the attributes or variables and explores the association rule between a course and gender, and discovers the influence of gender in studying a course. The previous research with this dataset has applied only regression models and this is the first time to apply association rules.

Mining association rules is an important task. Past transaction data can be analyzed to discover customer purchasing behaviors such that the quality of business decision can be improved. The association rules describe the associations among items in the large database of customer transactions. However, the size of the database can be very large. It is very time consuming to find all the association rules from a large database, and users may be only interested in the associations among some items. Moreover, the criteria of the discovered rules for the user requirements may not be the same. Many uninteresting association rules for the user requirements can be generated when traditional mining methods are applied. Hence, a data mining language needs to be provided such that users can query only interesting knowledge to them from a large database of customer transactions. A data mining language is presented. From the data mining language, users can specify the interested items and the criteria of the rules to be discovered. Also, an efficient data mining technique is proposed to extract the association rules according to the users requests.

The significance of this research lies in the application of data mining techniques to analyze badminton competitions, allowing for the identification of players’ tactics, strategies, and weaknesses. By uncovering these insights, targeted training and improvement plans can be developed, enhancing player performance and optimizing competitive outcomes. Association rules in data mining techniques are used, and the Apriori algorithm in association rules is analyzed and improved. The ACARMI algorithm (an improved algorithm of constrained association rule mining based on item) is then applied. Then, data from six top badminton competitions is collected and converted into Boolean data suitable for this algorithm. A data mining module is designed to mine the tactical association rules of all players, and a specific analysis is conducted on one of the players, successfully analyzing their tactical strategies. Finally, to verify the effectiveness of the algorithm proposed in this article, FP-Growth algorithm and DHP algorithm are applied to comparative experiments. The experiment shows that compared with the FP-Growth algorithm, the initial and final running times of the algorithm in this article with minimum support and different constraint numbers are around “355 ms and 75 ms,” “150 ms and 45 ms,” respectively; similarly, compared with the DHP algorithm, they are around “165 ms and 35 ms,” “175 ms and 55 ms,” respectively. Under two sets of experiments, the algorithm proposed in this article presents better operational efficiency. This article demonstrates that the use of data mining techniques can provide effective tactical optimization, opponent research, real-time decision support, and long-term development planning assistance for athletes and coaches.

Global gene expression profiling, both at the transcript level and at the protein level, can be a valuable tool in the understanding of genes, biological networks, and cellular states. As larger and larger gene expression data sets become available, data mining techniques can be applied to identify patterns of interest in the data. Association rules, used widely in the area of market basket analysis, can be applied to the analysis of expression data as well. Association rules can reveal biologically relevant associations between different genes or between environmental effects and gene expression. An association rule has the form LHS --> RHS, where LHS and RHS are disjoint sets of items, the RHS set being likely to occur whenever the LHS set occurs. Items in gene expression data can include genes that are highly expressed or repressed, as well as relevant facts describing the cellular environment of the genes (e.g. the diagnosis of a tumor sample from which a profile was obtained). We demonstrate an algorithm for efficiently mining association rules from gene expression data, using the data set from Hughes et al. (2000, Cell, 102, 109-126) of 300 expression profiles for yeast. Using the algorithm, we find numerous rules in the data. A cursory analysis of some of these rules reveals numerous associations between certain genes, many of which make sense biologically, others suggesting new hypotheses that may warrant further investigation. In a data set derived from the yeast data set, but with the expression values for each transcript randomly shifted with respect to the experiments, no rules were found, indicating that most all of the rules mined from the actual data set are not likely to have occurred by chance. An implementation of the algorithm using Microsoft SQL Server with Access 2000 is available at http://dot.ped.med.umich.edu:2000/pub/assoc_rules/assoc_rules.zip. Our results from mining the yeast data set are available at http://dot.ped.med.umich.edu:2000/pub/assoc_rules/yeast_results.zip.

Association rules provide an important clue on how hidden knowledge and patterns can be extracted and the attributes extracted are associated when big data is mined by means of a particular data mining algorithm. The context of HEIs, where big data mining is uncommon, there is a need to know how association rules could be utilized to discover hidden knowledge and forecast student performance based on time-to-degree and cumulative grade point average (CGPA) and also make decisions. This research has demonstrated this by taking the example of the dataset of 337 graduated students of a university in Bahrain belonging to the Bachelor's degree in Accounting and Finance. Two types of data mining concepts were applied, regression and a priori algorithm. Regression results showed that there is a statistically substantial link between time-to-degree and course-difficulty and not CGPA. Similarly, association rules showed the link between time-to-degree, course-taking pattern, no. of courses and course-difficulty pattern but not CGPA. The association rules generated provided the basis to predict optimum time-to-degree by controlling the no. of courses, course-taking pattern, and course-taking difficulty. The findings of this research provide an important window of opportunity for HEIs to enhance student performance using data mining techniques. This investigation has contributed theoretically showing the application of data mining theory using apriori algorithm and regression, for HEI big data mining where student factors and organizational factors are said to be fairly different from that of the HEIs where research on big data is seen to be a well-known activity.

Frequent pattern mining and consequently association rule mining is a useful technique for discovering relationships between items in databases. However, as the size of the data to be analyzed increases or the values of the pruning thresholds decrease, larger number of frequent pattern and more association rules will be generated with little information about the association rules in relation to each other. This research paper discusses a method to segment rules into different sets with no internal conflicts. The goal is to establish an effective method to reduce the difficulty for businesses to review the association rules of different customer segments, and track the behaviors of market segments based on their buying behaviors. The method established in this paper has the advantage of not needing customer information, thus removing the need for businesses to obtain customer information. This removes the threat of intrusions into customer privacy. The method also generates the rule sets based on conflicting rules, and dividing rules based on customer behaviors is more accurate than customer characteristics. The proposed method has been validated by running some tests.

Data mining is an essential technology to extract patterns or knowledge from large repositories of data. Association rules in market basket database represent the shopping behavior of customers. The association information may reveal trade secrets. It must be hidden before publishing. Association rule hiding in privacy preserving data mining hides sensitive rules containing sensitive items. In this paper, a new method is proposed to detect the sensitive items for hiding sensitive association rules. This proposed method finds the frequent item sets and generates the association rules. It employs the concept of representative association rules to detect sensitive items.

Dunia Vape Trading Business is a trading business engaged in the sale of electronic cigarette spare parts. Every consumer who is going to buy a product, the consumer looks at the product shelves one by one to find the product they are looking for. The problem that occurs is that sometimes it is tiring for consumers to search on different shelves or it can also cause consumers not to buy the products they actually need. This is because the products sold are not arranged based on the purchasing patterns that consumers often make. Therefore, a method is needed that can make it easier for consumers to find the desired product and so that UD Dunia Vape can find out consumer purchasing patterns so that it can arrange and provide products according to the usual patterns. Therefore, researchers utilize computer technology to help consumers find the desired product and so that UD Dunia Vape can find out consumer purchasing patterns so that it can arrange and provide products according to the usual patterns. In computer science, one of the methods that can be applied is by using data mining. One of the methods used in determining patterns is the Association Rule Mining Method. Association rule mining is a data mining technique that aims to find association rule patterns between a combination of items. Implementation of the Association Rule Mining Method on sales of goods at the UD Dunia Vape Store is used to provide information on the results of building material sales patterns each month which can be used as parameters in managing UD Dunia Vape's inventory of materials, in addition to providing information on the most popular and most sold building materials at UD Dunia Vape. The results obtained Based on the results and discussion, it can be concluded that the analysis of consumer purchasing patterns using the Association Rule method on Dunia Vape sales transaction data can be applied based on the results of manual method application where the association rule results are the same as data mining application testing, namely if you buy I = LOST VAPE URSA CAP POD KIT JOY PURPLE then Buy N = PODS FRIENDLY ICE PEDIA BLUEBERRY 15MG 30ML (24) with a confidence value of 100% and if you buy N = PODS FRIENDLY ICE PEDIA BLUEBERRY 15MG 30ML (24) then Buy I = LOST VAPE URSA CAP POD KIT JOY PURPLE with a confidence value of 100%.

Pattern mining algorithms for data streams using itemset

Multicollinearity Test This test aims to determine whether there is a correlation for the independent variables in the regression model used. A good regression model is a regression model that is non-multicollinearity, meaning that between one independent variable and another in the regression model are not perfectly interconnected. The size of the correlation that is free of multicollinearity is 5 or can be measured through the tolerance value or VIF (Variance Inflation Factor) of each variable in the SPSS ver. 21.0 computer program, namely if the tolerance value is 1.0 or VIF 5, it indicates the presence of multicollinearity. 21.0, namely if the tolerance value is 1.0 or VIF 5, it indicates the presence of multicollinearity. SPSS output ver. 20.0 to explain whether there are symptoms of multicollinearity between independent variables. Autocorrelation Test The autocorrelation test aims to test whether in a linear regression model there is a correlation between confounding errors in period t and confounding errors in period t-1 (previous). The following are the results of the autocorrelation test using SPSS 20.0:

Stock market prediction with high accuracy has always been an interesting subject for most investors and professional analysts. Data mining techniques are providing great aid to extract interesting and hidden knowledge from datasets. Financial data mining tools assist investors in their investment decisions, thereby reducing their investment risks. Associative classification rule mining is a promising approach in data mining that utilises the association rule discovery techniques to construct classification systems, also known as associative classifiers. This paper aims to develop an intelligent transaction system based on associative classification rule mining (ACR) and phenotypic artificial immune system (AIS) which discovers trading rules from numerical indicators. A new fitness function as a different measure of quality for quantitative association is suggested considering interestingness of rules. Based on the empirical studies on the top eight companies in the S&P 500 stocks, observed results demonstrate the superior prediction accuracy over the genetic algorithm based technique and the 'buy and hold' strategy.

Analysis of Transaction Database Using Apriori Algorithm. This study examines the use of the Apriori algorithm for analysing transaction databases. The Apriori algorithm is a fundamental technique in data mining that allows for the efficient discovery of frequent patterns and association rules in large datasets. The Apriori algorithm employs a two-step approach. Initially, it identifies frequent items in the database based on a user-defined minimum support threshold. Subsequently, it generates association rules that describe relationships between these frequent items based on metrics such as confidence and lift. This paper provides an in-depth explanation of the Apriori algorithm, emphasizing its strengths and limitations. Additionally, it presents various applications of the Apriori algorithm in real-world scenarios, such as shopping cart analysis, cross-selling and upselling, and customer segmentation. The significance of this study lies in its comprehensive analysis of the Apriori algorithm and its practical relevance in diverse data mining tasks. It serves as a valuable resource for researchers, practitioners, and anyone seeking to understand the fundamentals and applications of association rule mining. Keywords: Datamining, OLTP, Apriori algorithm, Item sets and Market Basket Analysis