Association Rules in Data Mining

IGI Global eBooks, 2011

International Journal of Computer Applications, 2013

Association rule mining has become particularly popular among marketers. In fact, an example of association rule mining is known as market basket analysis. The task is to find which items are frequently purchased together. This knowledge can be used by professionals to plan layouts and to place items that are frequently bought together in close proximity to each other, thus helping to improve the sales. Association rule mining involves the relationships between items in a data set. Association rule mining classifies a given transaction as a subset of the set of all possible items. Association rule mining finds out item sets which have minimum support and are represented in a relatively high number of transactions. These transactions are simply known as frequent item sets. The algorithms that use association rules are divided into two stages, the first is to find the frequent sets and the second is to use these frequent sets to generate the association rules. In this paper the applications, merits and demerits of these algorithms have been studied. This paper discusses the respective characteristics and the shortcomings of the algorithms for mining association rules. It also provides a comparative study of different association rule mining techniques stating which algorithm is best suitable in which case.

Webology, 2022

The establishment of a marketing strategy is important for every business actor in the competitive world of business. Business operators must be able to develop sound marketing strategies to influence the attractiveness of consumers and to buy interest in the products provided so that the enterprise they operate can compete and have a market share and to maximize sales sales. To implement marketing strategies, references are required so that promotions can reach the right target, for example by seeking similarities between items. By using data mining techniques, these studies apply the a priori approach to the promotion of customer product recommendations by association rules on product sales transaction datasets to aid in the formation of applications between product items. The dataset represents a sample of sales of products for 2020. The application used for analyzing is RapidMiner, where a support value of > 20% and confidence of > 60% is determined. Each product package promoted is made up of 2 products from the calculation results. The two best rules that have value confidence is combined with 2 items (Cre1→Cre2), (Cre1→Cre12) and (Cre9→Cre10). Based on the minimum support and confidence values that have been set, the results of the a priori method can produce association rules that can be used as a reference in product promotion and decision support in providing product recommendations to consumers.

2017

There are several mining algorithms of association rules. One of the most popular algorithms is Apriori that is used to extract frequent itemsets from large database and getting the association rule for discovering the knowledge. Based on this algorithm, this paper indicates the limitation of the original Apriori algorithm of wasting time for scanning the whole database searching on the frequent itemsets, and presents an improvement on Apriori by reducing that wasted time depending on scanning only some transactions. The paper shows by experimental results with several groups of transactions, and with several values of minimum support and minimum confidence that applied on the original Apriori and our implemented improved Apriori by means of the comparison with three parameters such as time Execution, Memory Consumed and Accuracy of Rules.

Data is one of the valuable resources for organization, and database management systems are gradually becoming ubiquitous in many small and medium scale companies. Although, some of the benefits of database management systems have been explored, however, many companies have not been able to exploit the advantages of gaining business intelligence from their databases. This has led to inadequate business decision making based on the data contained in the databases. In this paper, association rules mining also known as market basket analysis using Apriori algorithm is presented for extracting valuable knowledge embedded in the database of a supermarket. Data representing six (6) distinct products across thirty (30) unique transactions were generated from a well-structured transactional database representing the sales pattern of a supermarket store. The frequencies of purchasing these products were extracted for the above data and different association rules were deduced. It was established from these rules that purchase of one product would invariably lead to the purchase of another product as evident in the association between Apple and Chocolate. The discovered relationship will guide companies in planning marketing and advertising strategies that will help them outshine their competitors.

Association rule mining is one kind of data mining techniques, which discovers strong associations among data. The discovered rules may help market basket or cross-sales analysis, decision making, and business management. An example of such a rule is “60% of customers that buy jam also tend to buy butter; 25% of all customers buy both of these items.” Since these rules are easy to understand, explain, and catch some important relationships among the data in large databases, there is no wonder that mining association rules from large data sets has been a focused topic in recent research into data mining [2, 4, 20, 22, 25, 26, 27, 28, 29, 31, 33]. Similar to other mining tasks, mining association rules involves several major issues, including efficiency, In this paper, we propose and develop an interesting algorithm, called Apriori, for mining association rules, which enhances the recently developed Apriori algorithm [4] and integrates it with efficient association mining methods. The...

Aprioriis an algorithm for learning association rules. Apriori is designed to operate on databases containing transactions. As is common in association rule mining, given a set of item sets, the algorithm attempts to find subsets which are common to at least a minimum number candidate C of the item sets. Apriori uses a "bottom up" approach, where frequent subsets are extended one item at a time, and groups of candidates are tested against the data. The algorithm terminates when no further successful extensions are found. The purpose of the Apriori Algorithm is to find associations between different sets of data. It is sometimes referred to as "Market Basket Analysis". Each set of data has a number of items and is called a transaction. The output of Apriori is sets of rules that tell us how often items are contained in sets of data.

Retailers provide important functions that increase the value of the products and services they sell to consumers. Retailers value creating functions are providing assortment of products and services: breaking bulk, holding inventory, and providing services. For a long time, retail store managers have been interested in learning about within and cross-category purchase behavior of their customers, since valuable insights for designing marketing and/or targeted cross-selling programs can be derived. Especially, parallel to the development of information processing and communication technologies, it has become possible to transfer customers shopping information into databases with the help of barcode technology. Data mining is the technique presenting significant and useful information using of lots of data. Association rule mining is realized by using market basket analysis to discover relationships among items purchased by customers in transaction databases. In this study, associati...

Recently, data mining has attracted a great deal of attention in the information industry and in a Society where data continue to grow on a daily basis. The availability of huge amounts of data and the imminent need for turning such data into useful information and knowledge is the major focus of data mining. The information and knowledge obtained from large data can be used for applications ranging from market analysis, fraud detection, production control, customer retention, and science exploration. A record in such data typically consists of the transaction date and the items bought in the transaction. Successful organizations view such databases as important pieces of the marketing infrastructure. This paper considers the problem of mining association rules between items in a large database of sales transactions in order to understand customer-buying habits for the purpose of improving sales. Apriori algorithm was used for generating strong rules from inventory database. It was found that for a transactional database where many transaction items are repeated many times as a superset in that type of database, Apriori is suited for mining frequent itemsets. The algorithm was implemented using PHP, and MySQL database management system was used for storing the inventory data. The algorithm produces frequent itemsets completely and generates the accurate strong rules.

3rd International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics, 2017

Electronic commerce includes all business conduct through information and communication technology. Development of infrastructure, telecommunications, mobile technologies, the internet and social media in recent years, made a tremendous growth in business through e-commerce. Now e-commerce is a vital part of the economic development and helps in employment, FDI and GDP growth in the country. More and more companies are now on the internet and smooth the progress of transactions over the web. A large volume of data generated by these e-commerce sites which are updated very frequently. To increase the sell, customer retention and effective decision making, association rule mining play a significant role. There are number of association rule mining algorithms designed for e-commerce. A few algorithms also support the incremental and interactive association mining. In this paper, we conducted a comprehensive study of various association rule mining algorithms that support e-commerce transactions. The shortcoming of the various existing algorithms are also identified. Some plausible characteristics proposed as well for designing an efficient algorithm of e-commerce databases, which support incremental, interactive and multi-objective association rule mining.

2020

In competition in the business world, it is necessary to find the right strategy that can be used in sales optimization. Factors that influence the needs of market analysis is the level of frequency of consumers in buying an item. Because it is needed a solution to find sales patterns with the website to be more effective and efficient. The required data is taken from sales transaction data for a certain period and processed to produce association rules for goods and transactions. Besides being able to look for patterns that often appear among many transactions, this can make it easier for companies to increase sales turnover. The making of this application uses HTML as web page development, PHP as website development, and MySQL as database management. In the testing phase, this application starts from the login to get the results of the association analysis going well. Then from the conclusion of the application made with this application the manager can add more stock of goods to the product with the highest itemset, while for the lowest itemset marketing can be done by providing a package or discount for the purchase of these items.

International Journal on Natural Language Computing, 2014

There are several mining algorithms of association rules. One of the most popular algorithms is Apriori that is used to extract frequent itemsets from large database and getting the association rule for discovering the knowledge. Based on this algorithm, this paper indicates the limitation of the original Apriori algorithm of wasting time for scanning the whole database searching on the frequent itemsets, and presents an improvement on Apriori by reducing that wasted time depending on scanning only some transactions. The paper shows by experimental results with several groups of transactions, and with several values of minimum support that applied on the original Apriori and our implemented improved Apriori that our improved Apriori reduces the time consumed by 67.38% in comparison with the original Apriori, and makes the Apriori algorithm more efficient and less time consuming.

2018

Data mining (DM) techniques is the set of algorithms that helps in extracting interesting patterns and previously unknown facts from larger volume of databases. Todays ever changing customer needs, fluctuation business market and large volume of data generated every second has generated the need of managing and analyzing such a large volume of data. Association Rule mining algorithms helps in identifying correlation between two different items purchased by an individual. Apriori Algorithm and FP-Growth Algorithm are the two algorithms for generating Association Rules. This paper aims at analyze the performance of Apriori and FP-Growth based on speed, efficacy and price and will help in understanding which algorithm is better for a particular situation. https://journalnx.com/journal-article/20150659

Sigmod Record, 1993

We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an efficient algorithm that generates all significant association rules between items in the database. The algorithm incorporates buffer management and novel estimation and pruning techniques. We also present results of applying this algorithm to sales data obtained from a large retailing company, which shows the effectiveness of the algorithm.

European Scientific Journal, ESJ, 2016

In this study, it was aimed to investigate whether an association rule exists between the products sold, using the sales data of a supermarket with the data mining method within the framework of a customer-oriented approach. For this purpose, the Association Rule Mining Method was used, and analyses were carried out on existing data with the Apriori Algorithm that is widely used in this method. Various association rules were determined between the products sold as a result of these analyses. It was assessed that Association Rule Mining is an alternative technique to proactive customer orientation by revealing the latent purchasing behaviour patterns of the customers.