Market Basket Analysis

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...

Being negatively impressed by the data published by the European Commission in CARE (Community database on Accidents on the Roads in Europe), where Poland is presented as the European Country with the highest rate of fatalities in road crashes involving cyclists during 4 years period (2009–2013), the Authors decided to analyse available data. Bikes become a more and more popular means of transport and the way of active recreation. In Warsaw, the share of bicycle trips rises 1 to 3% per year. The aforementioned, together with increasing traffic density, caused 4233 registered injuries among cyclists in 2018 in Poland. In 286 cases the accidents were direct reasons for the cyclists’ death. Considering these facts, it becomes extremely important to point the most influencing factors and conditions contributing to cyclists’ serious accidents. Onedimensional or two-dimensional statistics are not sufficient to find all important associations between the road conditions and the number of c...

Association rule mining is the power ful tool now a days in Data mining. It identifies the correlation between the items in large databases. A typical example of Association rule mining is Market Basket analysis. In this method or approach it examines the buying habits of the customers by identifying the associations among the items purchased by the customers in their baskets. This helps to increase in the sales of a particular product by identifying the frequent items purchased by the customers. This paper mainly focuses on the study of the existing data mining algorithm for Market Basket data.

Motivated by the pursuit of academic excellence, Higher Education Institutions are increasingly using their students data in order to understand and improve their processes, courses, degree curricula, etc. When properly used, the empowerment reached by using data to support the decision-making processes, could provide a competitive advantage facing the international rankings. Market Basket Analysis, a very important data mining technique originated in retail sales analysis, will be applied to the analysis of the mathematics undergraduate degree of Instituto Superior Técnico, Universidade de Lisboa, using as input student curriculum footprints.

Knowledge is the most valuable asset in today's dynamic business environment. In many organizations, decisions are made based on a combination of judgment and knowledge extracted from databases. Successful business organization to be able to react rapidly to the changing market demands both locally and globally, by utilizing the latest data mining techniques of extracting previously unknown and potentially useful knowledge from vast resources of raw data. We propose a methodological framework for the use of the knowledge discovery process to improve store layout. In this paper, we propose a data driven decision support for store layout and present an empirical study. This paper develops a relational database and uses Apriori algorithm and multidimensional scaling techniques as methodologies for the store layout issue. As the empirical study, a supermarket analysis has done for Migros Turk A.S, a leading Turkish retailing company.

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.

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.

The following research is guided by the hypothesis, that products chosen on a shopping trip in a supermarket are an indicator of the preference interdependencies between different products or brands. The bundle chosen on the trip can be regarded as an indicator of a global utility function. More specific: the existence of such a function implies a cross?category dependence of brand choice behavior. It is hypothesized, that the global utility function related to a product bundle is the result of the marketing?mix of the underlying brands. To investigate the determinants of the choice for a certain bundle, a market basket forecast model is adopted from Russel and Petersen (2000) which uses a multivariate logistic function. The target of this paper is to apply a multivariate logistic approach to estimate a market basket model and to make a comparison between the results of the parameter estimates for a Canadian data set with a German one, which leads to a cross?cultural study. To our k...

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.

Apriori algorithm is one of the methods with regard to association rules in data mining. This algorithm uses knowledge from an itemset previously formed with frequent occurrence frequencies to form the next itemset. An a priori algorithm generates a combination by iteration methods that are using repeated database scanning process, pairing one product with another product and then recording the number of occurrences of the combination with the minimum limit of support and confidence values. The a priori algorithm will slow down to an expanding database in the process of finding frequent itemset to form association rules. Modification techniques are needed to optimize the performance of apriori algorithms so as to get frequent itemset and to form association rules in a short time. Modifications in this study are obtained by using techniques combination reduction and iteration limitation. Testing is done by comparing the time and quality of the rules formed from the database scanning using a priori algorithms with and without modification. The results of the test show that the modified a priori algorithm tested with data samplesof up to 500 transactions is proven to form rules faster with quality rules that are maintained.

The Cash-Based Interventions Technical Working Group is part of a broader effort to coordinate the humanitarian response for refugees in Turkey through the provision of life-saving and basic needs. The need for this TWG, as an action-oriented forum at the sub-national level in Gaziantep, Turkey, was identified by CBI actors in November 2015. With regards to its geographical focus, the CBI TWG will aim to address the needs of the most vulnerable refugees inside all of Turkey, with a focus on both camps and non-camp communities.

The CBI TWG, at an operational and technical level, is intended to be a mechanism of targeted information sharing, appropriate harmonization of approaches, determining and coordinating joint advocacy efforts, agreeing relevant minimum standards and planning to improve targeting of the most vulnerable households and increase their resilience to future shocks and stresses.

Its work is to capitalize on existing cash and voucher-based programming efforts from Government actors, the Turkish Red Crescent, UN and NGOs in order to ensure a more coordinated and effective response with minimal gaps and duplications and increased quality and accountability of programming.

Knowledge is the most valuable asset in today's dynamic business environment. In many organizations, decisions are made based on a combination of judgment and knowledge extracted from databases. Successful business organization to be able to react rapidly to the ...

Data mining is a technique that has become a widely accepted procedure for organizations in sourcing for data and processing it for decision making. Association rule mining is an aspect of data mining that has revolutionized the area of predictive modelling paving way for data mining technique to become the recommended method for business owners to evaluate organisational performance. Association rule mining (ARM) give top managers the opportunity to make informed business decisions by anticipating future movements and behaviours of customers. Market basket analysis (MBA) is paving the path in business as it has become the most widely used areas of data mining in marketing. This study defines association rule mining as a technique used to extract important patterns from existing information which enables better decision making in an establishment. MBA is a marketing strategy used by various organizations to find the optimal environments to advertise merchandise. A market basket comprises of products picked by a customer during the visit to a superstore. These work specifically focus on association rule mining algorithms and its application to MBA. This paper presents a critical review of various ARM algorithms, comparing each of the algorithms, and considering the merit and demerit of each. The outcome of the study shows that choosing an ARM algorithm for MBA depends on the data set size and the application area of MBA that the algorithm will be used, this is because according to the no free lunch theorem which state that no algorithm is guaranteed to outperform others in all domains hence the need for this study, to determine the performance of the algorithms. The study concluded by recommending a hybrid algorithm to be used for ARM in MBA systems.

A high monetary value of the construction projects is one of the reasons of frequent disputes between a general contractor (GC) and a client. A construction site is a unique, one-time, and singleproduct factory with many parties involved and dependent on each other. The organizational dependencies and their complexity make any fault or mistake propagate and influence the final
result (delays, cost overruns). The constant will of the parties involved results in completing a construction object. The cost increase, over the expected level, may cause settlements between parties difficult and lead to disputes that often finish in a court. Such decision of taking a client to a court may influence the future relations with a client, the trademark of the GC, as well as, its finance. To ascertain the correctness of the decision of this kind, the machine learning tools as decision trees
(DT) and artificial neural networks (ANN) are applied to predict the result of a dispute. The dataset of about 10 projects completed by an undisclosed contractor is analyzed. Based on that, a much bigger database is simulated for automated classifications onto the following two classes: a dispute won or lost. The accuracy of over 93% is achieved, and the reasoning based on results from DT and ANN is presented and analyzed. The novelty of the article is the usage of in-company data as the independent variables what makes the model tailored for a specific GC. Secondly, the calculation of the risk of wrong decisions based on machine learning tools predictions is introduced and discussed.

Dengan adanya revolusi industri 4.0, persaingan bisnis semakin ketat khususnya dari sisi proses bisnis promosi, penjualan, dan transaksi. Sebuah sistem sangat diperlukan untuk meningkatkan efisiensi dan daya saing industri melalui media internet atau sering disebut E-Commerce.Industri handmade di Indonesia masih kurang berkembang dalam hal pemasaran barang-barang yang telah diproduksi dikarenakan kurangnya penyebaran informasi kepada masyarakat. Kebanyakan proses transaksi jual beli berupa mekanisme penjualan yang berjalan sekarang masih konvensional sehingga menyebabkan pangsa pasar terbatas. Dengan adanya perangkat lunak E-Commerce berbasis Market Basket Analysis, diharapkankualitas pelayanan kepada pelanggan khususnya dalam memberikan informasi pilihan produk sekaligus meningkatkan proses promosi dan pemasaran produk handmade kepada masyarakat.

There are two main research traditions for analyzing market basket data that exist more or less independently from each other, namely exploratory and explanatory model types. Exploratory approaches are restricted to the task of discovering cross-category interrelationships and provide marketing managers with only very limited recommendations regarding decision making. The latter type of models mainly focus on estimating the effects of category-level marketing mix variables on purchase incidences assuming cross-category dependencies. We propose a procedure that combines these two modeling approaches in a novel two-stage procedure for analyzing cross-category effects based on shopping basket data: In a data compression step we first derive a set of market basket prototypes and generate segments of households with internally more distinctive (complementary) cross-category interdependencies. Utilizing the information on categories that are most responsible for prototype construction, se...

Frequent Itemset Mining is a Data Mining task that
has drawn the attention of researchers over the years. This
concept is used in Market Basket Analysis in particular and
Decision Support problems in general.In this paper, we have
focused on the developments in this area so far. We start with
an account of the algorithms that generate CandidateItemsets.
This class of algorithms proves costly, particularly in cases
where there exist a large number of Itemsets. Then we
describe the tree based Frequent Pattern algorithm that does
not require CandidateItemsetGeneration, thereby bringing the
cost down. After that, we introduce lattice based algorithms
in which fewer database scans are needed and hence I/O cost
gets reduced. Then we discuss an algorithm that uses a single
recursive function and simplifies its structure without
worrying too much about the speed. Then we move on to have
a look at an algorithm that uses hyperlinks and saves time
and space. Then we describe computationally efficient
algorithms for ClosedItemsets. Finally, we discuss some
algorithms that leverage the inherent advantages of some
special data representations to enhance efficiency. While
keeping the nub and essence intact, we have avoided the
original algorithmic and mathematical notations to keep it
perspicuous, coherent, and comprehensible. However, a
simplified block diagram has been given, wherever the nature
of the algorithm permits it, to summarize the functioning of
the algorithm briefly.

Data mining is the process of extracting interesting,
useful and previously unknown information or patterns from
large information repositories such as: relational database, data
warehouses, XML repository, etc. There are various types of data
mining techniques such as association rules, classifications and
clustering. Association rule mining is one of the most important
and well researched techniques of data mining. Among sets of
items in the transaction databases or other data repositories, it
seeks interesting correlations, frequent patterns, associations or
casual structures.  Association Rule  Mining is a very potential
technique which has the aim to find interesting and useful
patterns from the transactional database. It is mainly used in
market basket analysis that help to identify  patterns of  all those
items that are purchased together. To  denote association with
itemsets and their quantities, the Quantitative association mining
is used. In this, we partition each item into equi-spaced bins with
each bin representing a quantity range. It  assumes each bin as a
separate bin as we proceed with mining and we also take care to
reduce redundancies and rules between different bins of the same
item. Here, we make use of Association Rule Mining Technique
to create a platform which helps in grouping similar objects
together in a transaction process.

Retail managers have been interested in learning about cross-category purchase behavior of their customers for a fairly long time. More recently, the task of inferring cross-category relationship patterns among retail assortments is gaining attraction due to its promotional potential within recommender systems used in online environments. Collaborative filtering algorithms are frequently used in such settings for the prediction of choices, preferences and/or ratings of online users. This paper investigates the suitability of such methods for situations when only binary pick-any customer information (i.e., choice/non-choice of items, such as shopping basket data) is available. We present an extension of collaborative filtering algorithms for such data situations and apply it to a real-world retail transaction dataset. The new method is benchmarked against more conventional algorithms and can be shown to deliver superior results in terms of predictive accuracy.

Being negatively impressed by the data published by the European Commission in CARE (Community database on Accidents on the Roads in Europe), where Poland is presented as the European Country with the highest rate of fatalities in road crashes involving cyclists during 4 years period (2009–2013), the Authors decided to analyse available data. Bikes become a more and more popular means of transport and the way of active recreation. In Warsaw, the share of bicycle trips rises 1 to 3% per year. The aforementioned, together with increasing traffic density, caused 4233 registered injuries among cyclists in 2018 in Poland. In 286 cases the accidents were direct reasons for the cyclists’ death. Considering these facts, it becomes extremely important to point the most influencing factors and conditions contributing to cyclists’ serious accidents. Onedimensional or two-dimensional statistics are not sufficient to find all important associations between the road conditions and the number of c...

In India, the annual retail market is estimated to be between $450 billion and $500 billion (Rumman, 2011). The Indian government is currently examining an FDI proposal that would allow multi-brand retailers to own a stake of up to 51% in joint ventures with Indian partners, as is already the case for single-brand retailers like Marks & Spencer Group PLC and Nike Inc. In a scenario of such intense competition, analytics can be a major differentiator for the companies. In this study we have performed Market Basket Analysis for an Indian retail e-commerce portal and developed a model for assessing the analytical maturity of the company. Based on the study, the paper gives recommendations to the ecommerce portal and also makes suggestions on how the company can embed analytics in its DNA.

Data mining is the process of extracting interesting, useful and previously unknown information or patterns from large information repositories such as: relational database, data warehouses, XML repository, etc. There are various types of data mining techniques such as association rules, classifications and clustering. Association rule mining is one of the most important and well researched techniques of data mining. Among sets of items in the transaction databases or other data repositories, it seeks interesting correlations, frequent patterns, associations or casual structures. Association Rule Mining is a very potential technique which has the aim to find interesting and useful patterns from the transactional database. It is mainly used in market basket analysis that help to identify patterns of all those items that are purchased together. To denote association with itemsets and their quantities, the Quantitative association mining is used. In this, we partition each item into eq...

Apriori algorithm use an iterative approach where k-itemset used to explore (k+1)-itemset. (K+1)-itemset candidates containing subset frequency that rarely appears will not used in determining association rules. Association rules formed by "if antecedent then consequent". Implementation of the apriori algorithm was preceded by the preparation of transactions database and determination of minimum support and confidence. Apriori algorithm scanning database repeated, pair one item to another and record the number of occurrences in the overall transaction. Frequent itemset is determined by selecting a combination or itemset that the count value greater than or equal to the minimum support then calculated the percentage value of support and confidence of each candidate. The association rules selected from which fit the minimum support and confidence. Data used in this study was sample of 100 transactions from database point if sales. Final Association rules are obtained from implementation of apriori algorithm is "if HELLO PANDA REFIL then HELLO PANDA 10gr" with percentage of support 2.00% and confidence 100.00% and "if HELLO PANDA 10gr then HELLO PANDA REFIL" with percentage of support 2.00% and confidence 100.00%. So it can be concluded that most customers buy HELLO PANDA REFIL will also buy HELLO PANDA 10gr so goes otherwise. This study proves that the apriori algorithm suitable implemented to search the frequent itemset in the shopping cart. Association rules that formed of frequent itemset can be used as decision support in sales. Intisari-Algoritma apriori menggunakan pendekatan iteratif dimana k-itemset digunakan untuk mengeksplorasi (k+1)-itemset. Calon (k+1)-itemset yang mengandung frekuensi subset yang jarang muncul tidak dipakai menentukan aturan asosiasi. Aturan asosiasi berbentuk if antecedent then consequent. Implementasi algoritma apriori didahului dengan persiapan database transaksi serta penentuan batas minimum support dan confidence. Algoritma apriori melakukan scaning database berulang-ulang, memasangkan satu item dengan item lainnya dan mencatat jumlah kemunculan kombinasi dalam keseluruhan transaksi. Frequent itemset ditentukan dengan memilih itemset yang nilai kemuculannya diatas atau sama dengan nilai minimum support kemudian dihitung persentase nilai support dan confidencenya. Aturan asosiasi yang berlaku dipilih dari yang memenuhi syarat minimum support dan confidence. Data yang digunakan dalam penelitian ini adalah sampel 100 transaksi dari database point of sales. Aturan asosiasi final yang diperoleh dari penerapan algoritma apriori adalah "if HELLO PANDA REFIL then HELLO PANDA 10GR" dengan persentase support 2,00% dan confidence 100,00% dan aturan "if HELLO PANDA 10GR then HELLO PANDA REFIL" dengan persentase support 2,00% dan confidence 100,00%. Jadi dapat disimpulkan kebanyakan pelanggan membeli item HELLO PANDA REFIL juga akan membeli item HELLO PANDA 10GR begitu juga berlaku sebaliknya. Penelitian ini membuktikan bahwa algoritma apriori cocok diimplementasikan untuk mencari frequent itemset pada keranjang belanja. Aturan asosiasi yang dibentuk dari frequent itemset tersebut dapat dipakai sebagai pendukung keputusan dalam penjualan. Kata Kunci-Data Mining, Association Rules, Apriori Algorithm, Frequent Itemset, Item Combination, keranjang belanja