Study of customer behavior in online B2B shopping

The rapid development of the internet introduced new trend of electronic transactions that is gradually dominating all aspects of our daily life. The amount of data maintained by websites to keep track of the visitors is growing exponentially. Benefitting from such data is the target of the study described in this paper. We investigate and explore the process of analyzing log data of website visitor traffic in order to assist the owner of a website in understanding the behavior of the website visitors. We developed an integrated approach that involves statistical analysis, association rules mining, and social network construction and analysis. First, we analyze the statistical data on the types of visitors that come to the website, as well as the steps they take to reach and satisfy the goal of their visit. Second, we derive association rules in order to identify the correlations between the web pages. Third, we study the links between the web pages by constructing a social network based on the frequency of access to the web pages such that two web pages get linked in the social network if they are identified as frequently accessed together. The value added from the overall analysis of the website and its related data should be considered valuable for ecommerce and commercial website owners; the owners will get the information needed to display targeted advertisements or messages to their customers. Such an automated approach gives advantage to its users in the current competitive cyberspace. In the long run, this is expected to allow for the increase in sales and overall customer loyalty.

The Technology has an increasing impact on all areas of life, including the education sector, and requires developing countries to emulate developed countries and integrate technology into their education systems. Recently schools in Libya are facing an issue trying to figure out why students perform poorly in certain subjects and how can they know how they will perform next in the future in coming semesters in perspective subject. There are several methods proposed to predict the student’s performance, using data mining techniques. In this paper, there are plans to create Data Mining Techniques in Education (i.e., DME) prediction model clustering, classification and association rule mining in many universities and schools in order to provide students and teachers with the most advanced platform. Although relatively late, the Libyan government finally responded to this challenge by investing heavily in rebuilding the education system and launching a national plan to presented method in terms of predicting students’ performance based on their grades in Math and English. The results are divided in to three main sections clustering analysis using k-mean algorithm, classification analysis was done using two rounds first using Gain Ratio Evaluations to find out the top attributes that used by J84 algorithm in second round of classification, and rule association analysis using A priori algorithm. Rule association analysis is applied for the clusters generate by clustering analysis to generate the rules associated with each cluster. For each section, a list of inputs is presented with the scale used for the values followed by the results of the algorithm and explanation for the finding.

The online version contains supplementary material available at 10.1007/s40200-021-00884-2.

One of the main problems in dynamic customer segmentation is finding the dominant patterns of customer movements between different segments via time. Accordingly, we concentrate on the customer dynamics in this paper and try to find different groups of customers in transmissions between segments via time. The dominant characteristics of these groups are also investigated. To obtain this objective, a new hybrid technique based on the K-means algorithm, hierarchical clustering and association rule mining is presented and implemented on the data of one of the main telecommunication corporations in Iran. The results show that there are seven different groups of customers. Furthermore, the impact of customer dynamics on segments’ changes via time is investigated. In this regard, a new approach of categorizing customers is proposed according to their impact on the structure and the content of segments’ changes. These new groups include “the customers who preserve the structure”, “the ones who are consistent with the structure” and “the customers who destroy the structure”.

The number of mobile shop is growing very fast in various regions in Indonesia including in Yogyakarta due to the increasing demand of mobile phone. This fact leads high competition among the mobile phone shops. In these conditions the mobile phone shop should have a good promotion strategy in order to survive in competition, especially for a small mobile phone shop. To create attractive promotion strategy, the companies/shops should know their customer segmentation and the buying pattern of their target market. These kind of analysis can be done using Data mining technique. This study aims to segment customer using Agglomerative Hierarchical Clustering and know customer buying pattern using Association Rule Mining. This result conducted in a mobile shop in Sleman Yogyakarta. The clustering result shows that the biggest customer segment of the shop was male university student who come on weekend and from association rule mining, it can be concluded that tempered glass and smart phone “x” as well as action camera and waterproof monopod and power bank have strong relationship. This results that used to create promotion strategies which are presented in the end of the study.

Data mining in agriculture is a modern and emerging research technique. Data mining provide many techniques like k means algorithm, support vector machine, association rule mining and Bayesian belief network [1]. This technique can be used in agriculture for various purposes. This paper describes about how association rules mining and apriori algorithm can be used in agriculture field. This paper also describes about soil, its types and crops grown in each type of soil. The technique that has been used here can be a rough set study, but like this many efficient techniques can be applied to solve many problems in agriculture.

There is a huge information stockpile available on the internet. But the available information still throws a stiff challenge to users while selecting the needed information. Such an issue can be solved by applying information filtering for locating the required information through a Recommender System. While using a RS, the users find it easy to curate and collect relevant information out of massive databanks. Though various types of RS are currently available, yet the RS developed by Collaborative Filtering techniques has proven to be the most suitable for many problems. Among the various Recommended Systems available, movie recommendation system is the most widely used one. In this system, the recommendations will be made based on the similarities in the characteristics as exhibited by users / items. The movie recommendation system contains a huge list of user objects and item objects. This paper combines Collaborative Filtering Technique with association rules mining for better compatibility and assurance while delivering better recommendations. Hence, in the process, the produced recommendations can be considered as strong recommendations. The hybridization involving both collaborative filtering and association rules mining can provide strong, high-quality recommendations, even when enough data is unavailable. This article combines various recommendations for creating a movie recommendation system by using common filtering techniques and data mining techniques

An automated technique for identifying associations between medications, laboratory results and problems

Since the era of data explosion, data mining in transactional databases has become more and more important. There are many data mining techniques like association rule mining, the most important and well-researched one. Furthermore, frequent itemset mining is one of the fundamental but time-consuming steps in association rule mining. Most of the algorithms used in literature find frequent itemsets on search space items having at least a minsup and are not reused for mining next time. To deal with this problem, NOV-FI algorithms are proposed as a new approach in order to quickly detect frequent itemsets from transactional databases using an array of co-occurrences and occurrences of kernel item in at least one transaction. NOV-FI algorithms are easily expanded in distributed systems. Finally, the experimental results show that the proposed algorithms perform better than other existing algorithms.

Predictive analytics has emerged as a powerful tool in the field of financial engineering, enabling organizations to forecast trends, optimize strategies, and mitigate risks. Data mining techniques play a crucial role in extracting meaningful patterns from large volumes of financial datasets, contributing to the development of predictive modeling techniques. The purpose of this paper is to explore the application of various data mining techniques in predictive analytics within the financial sector, with a focus on their effectiveness in risk management, fraud detection, and portfolio optimization. Machine learning algorithms, such as decision trees, support vector machines, and neural networks, are increasingly being employed to model complex financial systems and enhance predictive accuracy. Additionally, techniques like clustering, association rule mining, and time series analysis are used to uncover hidden relationships and temporal patterns in financial datasets. Despite the significant advancements in these techniques, challenges remain in terms of data quality, computational complexity, and interpretability of results. This paper addresses these challenges and provides a comprehensive review of the most widely used data mining methods in financial engineering. Furthermore, it discusses the potential for future advancements in data mining algorithms and their integration with other emerging technologies like artificial intelligence and big data analytics. The findings indicate that while data mining offers significant potential in financial analytics, careful consideration must be given to the selection of appropriate techniques and the proper handling of data to ensure reliable predictions. Ultimately, this paper aims to contribute to the ongoing development of robust predictive modeling techniques in financial engineering by emphasizing the importance of data mining as an indispensable tool.

Privacy is very important in Data Mining. Association rule mining, is a important technique, has already been applied in a wide range of areas. Large databases cannot be secured by encryption or other techniques because of complexities. These security issues can be divided into two categories: data hiding and knowledge hiding. This paper deals with the problem of association rule mining which preserves the confidentiality of each database i.e. Knowledge hiding. Knowledge hiding is concerned with the sanitization of confidential knowledge from the data. There are many methods to solve this problem. The one, presented in the paper is called support-based and confidence-based blocking schemes. Apriori algorithm is used for mining rule from database. Knowledge hiding is achieved using ISL and DSR. The improvement over sensitive rule hiding is proposed to offer more accuracy and security with smaller negative impact. In ever changing business environment like enterprises e-businesses, the old fashioned disclosure and database inference protection techniques are not capable to ensure complete data privacy. Privacy is important for the on line disclosure of private information Organizations should be able to evaluate the risk of disclosing information and should adopt new more efficient approaches for information disclosure control, in order to maintain their competitive edge in the market. Not only the data but also the hidden knowledge in the data should be made secure. The sensitive information can be extracted in the form of association rules with association rule mining tools. But this can jeopardize the privacy of the customers. Some other rule could be very critical for the company itself such as buying patterns of very rich customers. For example, an Internet based company may give up selling hardware and may concentrate on selling books and videos. Therefore a rule relating the customer buying patterns of hardware may no longer be sensitive for that company. It is desirable to apply a trivial algorithm that hides data by deleting it randomly. Data Mining is the process of discovering new patterns from large data sets involving methods from statistics and artificial intelligence but also database management. Privacy has become an important issue in Data Mining. Many methods have been brought out to solve this problem. This paper focuses on the problem of association rule mining, which hides the knowledge from each database. This paper reviews the major method of knowledge hiding. Association rule mining, as a very important technique, has already been applied in a wide range of areas. Knowledge hiding is concerned with the sanitization of confidential knowledge from the data. As a result of association rule mining, many useful association rules will be discovered, but at the same time, many privacy rules will also be exposed which do not want others to know. To solve this, limit the mining process, in order to keep these sensitive rules being hidden. There are so many methods to solve this problem. The one that is covered in the paper is just one kind of them, called support-based and confidence-based blocking schemes. Algorithm used For Knowledge Hiding is ISL algorithm or DSR algorithm (2). The main disadvantage of a block in algorithm is the fact that the dataset, apart from the blocked values, is not distorted. Thus, an adversary can disclose the hidden rules by identifying those generating item sets that contain question marks and lead to rules with a maximum confidence that lies above the minimum confidence threshold. If the number of these rules is small then the probability of identifying the sensitive ones among them becomes high. To avoid this problem, a method for selectively removing individual values from a database is introduced to prevent the discovery of a set of rules, while preserving the data for other applications. The problem of building privacy preserving algorithms is considered for one category of data mining techniques, the association rule mining. New metrics is introduced in order to demonstrate how security issues can be taken into consideration in the general framework of association rule mining (1).

This chapter is interested in discussing how to use data mining techniques to assist in achieving an acceptable level of quality of service of telecommunication systems. The quality of service is defined as the metrics which are predicated by using the data mining techniques, decision tree, association rules and neural networks. Routing algorithms can use this metric for optimal path selection which in turn will affect positively on the system performance. Also, in this chapter management axis using data mining techniques were handled, i.e., check the status of the telecommunication networks, role of data mining in obtaining optimal configuration, how to use data mining technique to assure high level of security for the telecommunication. The popularity of data mining in the telecommunications industry can be viewed as an extension of the use of expert systems in the telecommunications industry. These systems were developed to address the complexity associated with maintaining a huge network infrastructure and the need to maximize network reliability while minimizing labor costs (Liebowitz, J. 1988). The problem with these expert systems is that they are expensive to develop because it is both difficult and time consuming to elicit the requisite domain knowledge from experts.

The process of mining data and transforming it into useful patterns took longer duration with traditional data mining techniques. This work aims at providing faster mining and decision-making. It uses hash algorithm to promote speed and efficiency of rule mining procedures. Association rules are those that narrate the relationships prevailing between attributes present in the database. Extraction of those rules which are associated is termed as mining of Association Rules. The paper explains various rule interestingness measures which would help us in retrieving only effective, best and interesting rules. There exist so many numbers of gene in a human body which may or may not be associated with each other. This work attempts to find associations between the genes and lists out which are closely associated. Given the occurrence of a particular gene, the co-occurring genes and the genes that are no way related were analysed. The work has been implemented using RStudio of R data mining Toolkit with R language. It has been tested against various benchmark data sets and other algorithms. Based on various performance measurements, it is clear that the proposed algorithm excels to a better extent with the help of different rule interestingness measures when compared to others.

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.

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%.