Association Rules Mining

Mining on data reveals patterns that provide useful information for analysis, decision making and forecasting in various domains. Association Rule Mining (ARM) identifies patterns on itemsets which are either frequent or have interesting relationship amongst them based on strong rules and conceptually form a basis for Frequent Itemset mining (FIM) problems. FIM extracts binary values from transaction databases to identify frequently bought items but provides insufficient information for identifying infrequent items that generate maximum profit. So a latter problem, High utility itemsets (HUI) mining was developed to focus on the itemsets that generate huge profit to the business. Even though HUI is related to Business Intelligence, its application extends to Web Server Logs, Biological Gene Databases, Network Traffic Measurements and many other fields. This paper presents a survey on the algorithms from different aspects and perspectives based on Utility mining, Frequent Itemset generation and Association Rule Mining.

The World Wide Web is now the primary source for information discovery. A user visits websites that provide information and browse on the particular information in accordance with their topic interest. Through the navigational process, visitors often had to jump over the menu to find the right content. Recommendation system can help the visitors to find the right content immediately. In this study, we propose a two-level recommendation system, based on association rule and topic similarity. We generate association rule by applying Apriori algorithm. The dataset for association rule mining is a session of topics that made by combining the result of sessionization and topic modeling. On the other hand, the topic similarity made by comparing the topic proportion of web article. This topic proportion inferred from the Latent Dirichlet Allocation (LDA). The results show that in our dataset there are not many interesting topic relations in one session. This result can be resolved, by utilizing the second level of recommendation by looking into the article that has the similar topic.

Cement stabilized rammed earth (CRSE) is a sustainable, low energy consuming construction technique which utilizes inorganic soil, usually taken directly from the construction site, with a small addition of Portland cement as a building material. This technology is gaining popularity in various regions of the world, however, there are no uniform standards for designing the composition of the CSRE mixture. The main goal of this article is to propose a complete algorithm for designing CSRE with the use of subsoil obtained from the construction site. The article’s authors propose the use of artificial neural networks (ANN) to determine the proper proportions of soil, cement, and water in a CSRE mixture that provides sufficient compressive strength. The secondary purpose of the paper (supporting the main goal) is to prove that artificial neural networks are suitable for designing CSRE mixtures. For this purpose, compressive strength was tested on several hundred CSRE samples, with diffe...

In today’s age the major problem is related to the predicting user’s web page request. In the past few years the markov model is used for this problem. The effective web mining techniques like Clustering, Association rule mining and markov model having many drawbacks. We are proposing an approach for overcome that drawbacks which help us to improving search engine and helping user to find interesting web pages.

RapidMiner is a software for machine learning, data mining, predictive analytics, and business analytics. The server will record large web log files when user visits the website. Extracting knowledge from such huge data demands for new methods. In this paper, we propose a web usage mining method with RapidMiner. At first, the redundant files in log file are deleted by Matlab and then we mines web log which has been pretreated with RapidMiner, it obtains the custom of different user to visit the website by processing and analyzing log file, and mines unusual rules, and provides the reference for the policy decision and construction of website. Experimental result analysis show that, applying RapidMiner in web usage mining, will obtain frequent model which user visits the website, manage to optimize the website structure and recommends for users.

Over the recent years, the construction industry has been adapting the information technology (IT) in terms of computer design, construction documentation, maintenance, cost estimates, schedules (for example through BIM-Building Information Modelling), sales, procurement, etc. Gathered information about buildings, contracts, facilities, their use and maintenance generates a solid platform for the use of data mining. Using data mining methods in construction can improve the management and maintenance processes. Stakeholders like: architects, project managers, contractors, engineers, and owners can obtain important information from the databases using one of the numerous tools mentioned in the article (for example CART, MARS, ANN, SVM). Data mining can also allow for discovering of previously unknown dependencies in processes like construction training or decision-making. The article is a review of the data mining and machine learning methods used in construction. It presents current applications and academic research projects. Authors describe the phenomena, identify relevant publications, asses them, summarize and interpret significant findings. As a result, missing areas, trends and research opportunities are outlined.

Association rule mining (ARM) is the process of generating rules based on the correlation between the set of items that the customers purchase.Of late, data mining researchers have improved upon the quality of association rule mining for business development by incorporating factors like value (utility), quantity of items sold (weight) and profit. The rules mined without considering utility values (profit margin) will lead to a probable loss of profitable rules. The advantage of wealth of the customers' needs information and rules aids the retailer in designing his store layout[9]. An algorithm CSHURI, Customer Segmentation using HURI, is proposed, a modified version of HURI [6], finds customers who purchase high profitable rare items and accordingly classify the customers based on some criteria; for example, a retail business may need to identify valuable customers who are major contributors to a company's overall profit. For a potential customer arriving in the store, which customer group one should belong to according to customer needs, what are the preferred functional features or products that the customer focuses on and what kind of offers will satisfy the customer, etc., finds the key in targeting customers to improve sales [9], which forms the base for customer utility mining.

Frequent-Pattern Tree (FP-Tree) algorithm plays a vital role in mining associations, patterns and other data mining related jobs. Currently a software risk mitigation intelligent decision network engine using rule based technique has been designed against software risk factors. A novel idea of finding the associations between different software risk mitigation factors using FP-Tree algorithm has been proposed in this research work. Huge volumes of software risk factors and software risk mitigation factors exist in software development industry. That’s why data mining researchers have been attracted towards it to find something to explore. Software risk mitigation frequent patterns will be helpful for the project managers and software developers to make decisions against the software risk factors.

The emergence of big data analytics as a way of deriving insights from data brought excitement to mathematicians, statisticians, computer scientists and other professionals. However, the absence of a mathematical foundation for analytics has become a real challenge amidst the flock of big data marketing activities, especially in healthcare insurance. This paper developed a mathematical model for the analytics of healthcare insurance data using set theory. We implemented a prototype of the model using Java Programming Language, MapReduce Framework, Association Rule Mining and MongoDB. Also, we tested the model accuracy using data from the National Health Insurance Scheme in Nigeria to reduce delay. The result showed that the accuracy level was 97.14% on average, which depicts a higher performance for the model. This result implies that delays affecting the processing of data submitted by the providers and enrollees to the HMOs reduced drastically leading to the improvement in the eas...

Market-Basket Analysis is a process to analyse the habits of buyers to find the relationship between different items in their market basket. The discovery of these relationships can help the merchant to develop a sales strategy by considering the items frequently purchased together by customers. In this research, the data mining with market basket analysis method is implemented, where it can analyse the buying habit of the customers. The testing is conducted in Minimarket X. Searching for frequent item sets performed by Apriori algorithm to get the items that often appear in the database and the pair of items in one transaction. Pair of items that exceed the minimum support will be included
into the frequent item sets are selected. Frequent item sets that exceed the minimum support
will generate association rules after decoding. One frequent item sets can generate association
rules and find the confidence. The test results show, the application can generate the
information what kind of products are frequently bought in the same time by the customers
according to Hybrid-dimension Association Rules criteria. Results from the mining process
show a correlation between the data (association rules) including the support and confidence
that can be analysed. This information will give additional consideration for owners of
Minimarket X to make the further decision.

Association rule mining (ARM) is the process of generating rules based on the correlation between the set of items that the customers purchase.Of late, data mining researchers have improved upon the quality of association rule mining for business development by incorporating factors like value (utility), quantity of items sold (weight) and profit. The rules mined without considering utility values (profit margin) will lead to a probable loss of profitable rules. The advantage of wealth of the customers' needs information and rules aids the retailer in designing his store layout[9]. An algorithm CSHURI, Customer Segmentation using HURI, is proposed, a modified version of HURI [6], finds customers who purchase high profitable rare items and accordingly classify the customers based on some criteria; for example, a retail business may need to identify valuable customers who are major contributors to a company's overall profit. For a potential customer arriving in the store, which customer group one should belong to according to customer needs, what are the preferred functional features or products that the customer focuses on and what kind of offers will satisfy the customer, etc., finds the key in targeting customers to improve sales [9], which forms the base for customer utility mining.

Customer’s segmentation is used as a marketing differentiation tool which allows organizations to understand their customers and build differentiated strategies. This research focuses on a database from the SMEs sector in Colombia, the CRISP-DM methodology was applied for the Data Mining process. The analysis was made based on the PFM model (Presence, Frequency, Monetary Value), and the following grouping algorithms were applied on this model: k-means, k-medoids, and Self-Organizing Maps (SOM). For validating the result of the grouping algorithms and selecting the one that provides the best quality groups, the cascade evaluation technique has been used applying a classification algorithm. Finally, the Apriori algorithm was used to find associations between products for each group of customers, so determining association according to loyalty

In this work, we aim at enhancing personalization by detecting students who may fail their courses and those who are more likely to succeed. After that, we will provide recommendations in each case. We seek to prevent the failure of at-risk students as well as increasing successful students' performance.

Our recommendation is based on a robust classification layer designed to efficiently categorize students and then offering the most suitable guidance for each category using different pattern recognition algorithm.

Association rule mining is becoming increasingly prevalent and important for deriving meaningful information from large datasets. However, traditional methods of association rule mining do not take into account the relative importance and influence of items within the dataset. To further complicate this, it is not always known what the appropriate drivers for weights are or the appropriate drivers are too difficult to compute or approximate. To address the problem of automatic assignment of weights to items within a dataset, this dissertation introduces two methods Page Rank and Principal Components Analysis (PCA) for automatic item weight assignment. These automatic item weight assignment techniques take into consideration the number of relationships and the strength of the relationships between items within the dataset, to generate weights that indicate the degree of influence that the items have within the dataset.
The research conducted in this paper found that both Page Rank and PCA are suitable techniques for automatic item weight generation and both methods generate rules containing similar items, though ranked differently.

The paper represents the data mining techniques used for predicting student's performance. In today's world the education field is growing, developing widely and becoming one of the most crucial industries. The data available in the educational field can be studied using educational data mining so that the unseen knowledge can be obtained from it. In this paper, various Data Mining approaches like Association mining and classification are used to predict the students' performance in examination in advance, so that necessary measures can be taken to improvise on their performance to score better marks. The results obtained after the implementation may be useful for instructor as well as students. This work will help in taking appropriate decision to improve student's performance.

The impact and popularity of competition concept has been increasing in the last decades and this concept has escalated the importance of giving right decision for organizations. Decision makers have encountered the fact of using proper scientific methods instead of using intuitive and emotional choices in decision making process. In this context, many decision support models and relevant systems are still being developed in order to assist the strategic management mechanisms. There is also a critical need for automated approaches for effective and efficient utilization of massive amount of data to support corporate and individuals in strategic planning and decision-making. Data mining techniques have been used to uncover hidden patterns and relations, to summarize the data in novel ways that are both understandable and useful to the executives and also to predict future trends and behaviors in business. There has been a large body of research and practice focusing on different data mining techniques and methodologies. In this study, a large volume of record set extracted from an outpatient clinic's medical database is used to apply data mining techniques. In the first phase of the study, the raw data in the record set are collected, preprocessed, cleaned up and eventually transformed into a suitable format for data mining. In the second phase, some of the association rule algorithms are applied to the data set in order to uncover rules for quantifying the relationship between some of the attributes in the medical records. The results are observed and comparative analysis of the observed results among different association algorithms is made. The results showed us that some critical and reasonable relations exist in the outpatient clinic operations of the hospital which could aid the hospital management to change and improve their managerial strategies regarding the quality of services given to outpatients.

Data is raw facts and figures and information is meaningful data that would be helpful for a person or company. Business intelligence extracts information from raw data through tools like data mining, perspective analysis, online analytical processing etc. Business Intelligence (BI) can be used to support a wide range of business decisions ranging from operational to strategic.
The textbook will provide comprehensive information to readers about business intelligence and analytics.

ssociation rule mining is a rule-based machine learning method which is used for discovering relationships and patterns between various items in large datasets. For example, association rule mining discovers regularities between products in large scale transactions, as we can see in point-of-sale systems of supermarkets. This will help extensively in marketing activities such as ‘product placements’ and ‘pricing’.Association rule mining also has other applications such as web usage mining, intrusion detection, bioinformatics etc.In this project, we have discussed association rule mining and its application for market basket analysis. We have discussed the calculation and importance of various metrics like support, confidence, lift, all-confidence, conviction. A case study was done, using Python programming language to analyse a departmental store data consisting of 7501 records and found the association rules with their corresponding metrics. We have used the apriori function for the process. For better understanding and visualisation, we have plotted the rules and made a combined effort to infer the best possible rule.

The aim of this paper is to find out the patterns of incidents in a steel plant in India. Occupational incidents occur in steel plant mainly in form of injury, near miss, and property damage or in combination. Different factors are responsible for such incidents to occur. An incident investigation scheme is proposed. Association rule mining approach is used to discover cause-and-affect patterns (rules) using 843 incidents. Thirty-five meaningful association rules are extracted using three criteria, support (S), confidence (C) and lift (L). For example, the results show that unsafe acts done by others are more frequent in injury cases (S = 4.86%, C = 78.8%, L = 2.3). Similarly, one of the SOP (standard operating procedures) related rule: ‘SOP required, available, adequate but not complied’ led to property damage (S = 11.03%, C = 49.2%, L = 1.525). Another useful rule ‘SOP required, available but inadequate, followed’ led to near miss (S = 1.66%, C = 38.89%, L = 1.163). It is also found that for slip, trip and fall incidents, workers working alone (S = 3.91%, C = 76.74%, L = 2.239) or in a group (S = 3.20%, C = 75.00%, L = 2.188) does not make much difference. The findings pinpoint the areas of improvement such as inadequate SOPs, non-compliance of SOPs, training, and slip, trip and fall prevention to minimize incidents.

Flight delays hurt airlines, airports, and passengers. Their prediction is crucial during the decision making process for all players of commercial aviation. Moreover, the development of accurate prediction models for flight delays became cumbersome due to the complexity of air transportation system, the number of methods for prediction, and the deluge of flight data. In this context, this paper presents a thorough literature review of approaches used to build flight delay prediction models from the Data Science perspective. We propose a taxonomy and summarize the initiatives used to address the flight delay prediction problem, according to scope, data, and computational methods, giving particular attention to an increased usage of machine learning methods. Besides, we also present a timeline of significant works that depicts relationships between flight delay prediction problems and research trends to address them.

The published version of this paper is made available at https://doi.org/10.1080/01441647.2020.1861123.
Please cite as:
L. Carvalho, A. Sternberg, L. Maia Gonc¸alves, A. Beatriz Cruz, J.A. Soares, D. Brand˜ao, D. Carvalho, e E. Ogasawara, 2020, On the relevance of data science for flight delay research: a systematic review, Transport Reviews

Most of the established companies have accumulated masses of data from their
customers for decades. With the e-commerce applications growing rapidly, the companies
will have a significant amount of data in months not in years. Data Mining,
also known as Knowledge Discovery in Databases (KDD), is to find trends, patterns,
correlations, anomalies in these databases which can help us to make accurate future
decisions.
Mining Association Rules is one of the main application areas of Data Mining.
Given a set of customer transactions on items, the aim is to find correlations between
the sales of items.
We consider Association Mining in large database of customer transactions. We
give an overview of the problem and explain approaches that have been used to attack
this problem. We then give the description of the Apriori Algorithm and show results
that are taken from Gima Türk A.Ş. a large Turkish supermarket chain. We also
use two statistical methods: Principal Component Analysis and k-means to detect
correlations between sets of items.

In recent times, customer behaviour models are typically based on data mining of customer data, and each model is designed to answer one question at one point in time. Predicting customer behaviour is an uncertain and difficult task. Thus, developing customer behaviour models requires the right technique and approach. Once a prediction model has been built, it is difficult to manipulate it for the purposes of the marketer, so as to determine exactly what marketing actions to take for each customer or group of customers. Despite the complexity of this formulation, most customer models are actually relatively simple. Because of this necessity, most customer behaviour models ignore so many pertinent factors that the predictions they generate are generally not very reliable. This paper aims to develop an association rule mining model to predict customer behaviour using a typical online retail store for data collection and extract important trends from the customer behaviour data.

The management and organization of archaeological data in complex database management systems (DBMS), and more recently data warehouses, such as the Online Cultural and Historical Research Environment (OCHRE), has become commonplace within the discipline. Traditionally, Near Eastern archaeologists have emphasized data interoperability and integration for inter-site comparison
of archaeological data, often through attempts to establish data “standards,” which has proven relatively unfruitful. Furthermore, as highly atomized, item-based data structures come to replace the traditional relational data model often utilized by archaeologists, it has become apparent that a data standard is not necessary as long as our data ontologies remain appropriately abstract (i.e., highly itemized). In doing so, we can conceive of DBMS not as simple repositories of data, but as analytical environments in their own right, wherein lies the potential for not only the accumulation, but the construction and contestation of knowledge from archaeological data (Knowledge Discovery from Archaeological Data [KDAD]). Following a brief excursus into the phenomenon of data mining and its implications within the discipline, this paper will focus on the implementation of a revised apriori algorithm (after Agrawal and Srikant 1994) for establishing associations among items in a dataset. The algorithm is applied to a dataset from Tell Mastuma, Syria, to elucidate heretofore unrecognized patterns among the data derived from Stratum I-2b. It is argued such patterns are indicative of past practices enacted at the site, and association rules are utilized to paint a more nuanced portrait of the community of Tell Mastuma during the Iron II.

In mining operations carried out below the water table, mine area could potentially affect the surrounding. With further deepening of the mine and quarry, the drawdown can impact on water supply wells and base flow. The variation in radius of influence under confined and unconfined aquifer condition is assessed using a hypothetical case study with three operational quarry sites. Average pit-wise radius of influence (R0) for the pit mine, under unconfined and confined aquifer conditions is 963 m and 61 km, respectively, whereas effective radius (Re) for total (cumulative) excavated mine area is 1.7 and 146 km, respectively. It means that the maximum and minimum value of overall impact/influence for the unconfined aquifer lies in-between 963 m and 1.7 km. Similarly that the maximum and minimum value of overall impact/influence for the confined aquifer lies in-between 61 and 146 km. Meanwhile, Re or effective radius for total (cumulative) excavated area seems more appropriate from evaluation viewpoint due to the overlapping (superposition) concentric mining quarries. Thus, assessment of quarry-wise and total radius of influence is of importance to fulfil targeted production, economically with minimum interruptions as part of the mine planning. Moreover, it helps us to assess, monitor and regulate the impact of a mine-dewatering program in the area. Also, the pumping test is often used to estimate radius of influence; it can trigger the inflow or radius of influence. Estimate of the radius of influence using analytical equation mainly independent of discharge/abstraction from the aquifer stands as a preferred choice for the prediction of radius of influence at the quarry/mine pit. There is a strong rationale that policies should be informed by such analytical assessment because it helps us to prevent or minimize negative impacts as part of regulation and pit-mining management and are useful tools for practitioners to design economically efficient and cost-effective in situ groundwater remediation systems, to contain contaminant plumes, to evaluate the surface–subsurface water interaction and to verify numerical models.

Software industry has been rapidly growing from the last couple of decades. Due to this growth and advancement many issues are going to be occurred. There are number of factors that affect the whole software development process. This research has focused on the most important risk factors that affect the overall software project and risk mitigation reviewed from the literature and my own experience. The main aim of this research is to collect the most important risk factors and risk mitigation for the development of intelligent network using an Artificial Intelligence technique known as Rule Based System. This is helpful for the project managers to make decisions. For the accomplishment of this task Firstly an exploratory survey has been conducted to prioritize the twenty requirements and fifty software risk mitigation factors. A sample of 200 respondents is selected out of which 100 are the university students and 100 are the IT professionals of a well reputed software house. Secondly a Rule Based technique is used to generate the risk mitigation intelligent network. Rules have been stored in the form of risk mitigation in relationship with risk factors. The system follows five steps of execution. First of all risk factors has been prioritized based upon the associations of risk factors and risk mitigation. Secondly, it creates new relationships between them. Thirdly, enter these relationships as rules into the Knowledge Base or Rule Base. At the fourth stage, an RBS engine executes these rules and finally an intelligent risk mitigation network is generated.

The well-known and most used support-confidence framework for Association rule mining has some drawbacks when employ to generate strong rules; this weakness has led to its poor predictive performances. This framework predicts customers buying behavior based on the assumption of the confidence value, which limits its competent at making a good business decision. This work presents a better Association Rule Mining conceptualized framework for mining previous customers transactions dataset of the grocery store for the optimal prediction of products placed on the shelves, physical shelf arrangement and identification of products that needs promotion. Sampled transaction records were used to demonstrate the proposed framework. The proposed framework leverage the ability of lift metric at improving the predictive performance of Association Rule Mining. The Lift discloses how much better an association rule predicts products to be placed together on the shelf rather than assuming. The proposed conceptualized framework will assist retailers and grocery store owners to easily unlock the latent knowledge or patterns in collected grocery's store transaction dataset to make important business decisions that will make them competitive and maximize their profit margin.

More and more devices featuring internet connectivity are being created every day. The value of such devices is in their ability to interact with one another, as part of automation workflows created by users to improve their home experience. Various platforms offer systems that allow for the creation of automation rules between different connected devices, but formulating and defining such automations can be quite complex.
This work explores the automated, personalized recommendation of automation rules for users of connected devices. The application of recommender systems’ techniques to this domain is novel,
so that exploring the applicability of the recommendation approaches discussed in the literature is one of the main contributions of this work. The hypothesis that certain groups of automations provide synergies was explored, and a system that exploits this by providing recommendations based on learning association rules was developed. Various strategies to make association rule mining feasible
on an automation dataset were devised, such as generalizing automation rules into recommendable items, and applying a similarity operator to all items, making automations identifiable across users.
The developed system showed very promising results, under an evaluation methodology consisting of gathering precision, recall and coverage metrics for the most important hyperparameters, as well as when comparing it against a naive recommender developed as a baseline. Finally, it was discussed how the techniques developed in this work are generalizable to domains outside of automation rule territory.

The intent of this chapter is to re-imagine and apply the term " civil disobedience " to the cyber domain. The full extent of how it can apply will be analyzed in a framework of the evolutionary rights of protest. Traditional civil disobedience is a " performance " via visual and physical mediums, while cyber disobedience is essentially a digital performance with difficulty in attributing true ownership of the act. This is an interesting antithesis to the physical protestor, who uses strategic visibility, threat of arrest, and popular backlash against state behavior as vital aspects of the disobedience. These variables are noticeably absent in cyber disobedience.

Text mining is the process of extracting interesting and non-trivial knowledge or information from unstructured text data. Text mining is the multidisciplinary field which draws on data mining, machine learning, information retrieval, computational linguistics and statistics. Important text mining processes are information extraction, information retrieval, natural language processing, text classification, content analysis and text clustering. All these processes are required to complete the preprocessing step before doing their intended task. Pre-processing significantly reduces the size of the input text documents and the actions involved in this step are sentence boundary determination, natural language specific stop-word elimination, tokenization and stemming. Among this, the most essential and important action is the tokenization. Tokenization helps to divide the textual information into individual words. For performing tokenization process, there are many open source tools are available. The main objective of this work is to analyze the performance of the seven open source tokenization tools. For this comparative analysis, we have taken Nlpdotnet Tokenizer, Mila Tokenizer, NLTK Word Tokenize, TextBlob Word Tokenize, MBSP Word Tokenize, Pattern Word Tokenize and Word Tokenization with Python NLTK. Based on the results, we observed that the Nlpdotnet Tokenizer tool performance is better than other tools.

The Apriori algorithm that mines frequent itemsets is one of the most popular and widely used data mining algorithms. Now days many algorithms have been proposed on parallel and distributed platforms to enhance the performance of Apriori algorithm. They differ from each other on the basis of load balancing technique, memory system, data decomposition technique and data layout used to implement them. The problems with most of the distributed framework are overheads of managing distributed system and lack of high level parallel programming language. Also with grid computing there is always potential chances of node failures which cause multiple re-executions of tasks. These problems can be overcome by the MapReduce framework introduced by Google. MapReduce is an efficient, scalable and simplified programming model for large scale distributed data processing on a large cluster of commodity computers and also used in cloud computing. In this paper, we present the overview of parallel Apriori algorithm implemented on MapReduce framework. They are categorized on the basis of Map and Reduce functions used to implement them e.g. 1-phase vs. k-phase, I/O of Mapper, Combiner and Reducer, using functionality of Combiner
inside Mapper etc. This survey discusses and analyzes the various implementations of Apriori on MapReduce framework on the basis of their distinguishing characteristics. Moreover, it also includes the advantages and limitations of MapReduce framework.

Data mining is the art and science of intelligent
analysis of (usually large) data sets for meaningful (and
previously unknown) insights and is nowadays actively applied
in a wide range of disciplines related to agriculture. Association
Rule Mining is a powerful tool for generating rules from vast and
diversified data  such as agricultural datasets. Due to the
emerging importance of data mining techniques and
methodologies in the area of agriculture, this paper is a survey of
some previous researches done in this field. For sustainable
growth of agriculture, these methodologies need to be monitored
and analyzed optimally

This project dealt with carrying out market basket analysis on two real-world datasets using association rule mining. Various metrics of association rules like "support", "confidence", "lift", "leverage", "coverage", and "conviction" are first explained, and then two case studies were carried out for analyzing the behavior of online and physical customers. Extensive results are presented based on our observations from the study.

With the e-commerce applications growing rapidly, the companies have a significant amount of data in
their hands. Data Mining is one of the methods for extracting useful information from this raw data.
The aim of Data Mining is to find relations in the data.
Mining Association Rules (Market Basket Analysis) is one of the main application areas of Data
Mining. Given a set of customer transactions on items, the aim is to find correlations between the
sales of items.
We applied Market Basket Analysis to some subset of the data taken from some stores of Gima Türk
A.Ş. and we obtained promising results.