Re-mining Positive and Negative Association Mining Results

Journal of Research in Engineering and Applied Sciences, 2017

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.

Communications in Statistics - Theory and Methods, 2020

This research analyzes the shopping basket by using association rules in the retail area, specifically in a home goods sales company such as appliances, computer items, furniture, and sporting goods. With the rise of globalization and the advancement of technology, retail companies are constantly struggling to maintain and raise their profits and offer the products and services that the customer wants to obtain. In this sense, they need a new approach to identify different objectives to be more competitive and successful, looking for new decision-making strategies. By providing large amounts of data collected in business transactions, the need arises to intelligently analyze such data to extract valuable knowledge that will support decision-making and understand the association patterns that occur in sales-customer behavior. Predicting which product will make the most profit, products sold together, this type of information is of great value for storing products in the inventory. Knowing when a product is out of fashion can support inventory management effectively. In this sense, this work presents the rules of association of products obtained by analyzing the data with the FPGrowth algorithm using the Orange tool.

2013

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.

Abstracts -Association rule mining identifies the remarkable association or relationship between a large set of data items. With huge quantity of data constantly being obtained and stored in databases, several industries are becoming concerned in mining association rules from their databases. For example, the detection of interesting association relationships between large quantities of business transaction data can assist in catalog design, cross-marketing, lossleader analysis, and various business decision making processes. A typical example of association rule mining is market basket analysis. This method examines customer buying patterns by identifying associations among various items that customers place in their shopping baskets. The identification of such associations can assist retailers expand marketing strategies by gaining insight into which items are frequently purchased jointly by customers. It is helpful to examine the customer purchasing behavior and assists in increasing the sales and conserve inventory by focusing on the point of sale transaction data. This work acts as a broad area for the researchers to develop a better data mining algorithm. This paper presents a survey about the existing data mining algorithm for market basket analysis.

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.

Decision making and understanding the behavior of the customer has become vital and challenging problem for organizations to sustain their position in the competitive markets. Technological innovations have paved breakthrough in faster processing of queries and sub-second response time. Data mining tools have become surest weapon for analyzing huge amount of data and breakthrough in making correct decisions. The objective of this paper is to analyze the huge amount of data thereby exploiting the consumer behavior and make the correct decision leading to competitive edge over rivals. Experimental analysis has been done employing association rules using Market Basket Analysis to prove its worth over the conventional methodologies.

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...

International Journal of Advance Research and Innovative Ideas in Education, 2017

Association Rule Mining (ARM) aims to identify the purchasing patterns of customer. The purpose is to discover the concurrence association among data in large database & to discover interesting association between attributes in databases. The main aspect of ARM is to find frequent item set generation & Association Rule generation. In this paper we concentrate on frequent pattern mining Algorithms. This research paper discusses the comparison between three minig Algorithms i.e. Apriori Algorithm, Eclat Algorithm, and Improved Apriori Algorithm. It also focuses on advantages & disadvantages of these algorithms. The comparison is done w.r.t Market Basket Analysis using Hadoop. Mining of association rules from frequent pattern mining from massive collection of data is of interest for many industries which can provide guidance in decision making process such as cross marketing or arrangement of item in Stores & Supermarkets.

IJSRCSEIT, 2018

In this study, we focused on how data mining can be applied in Market Basket Analysis to identify new trend sand purchasing patterns of customers. Data Mining is the process of extracting useful information from a large dataset. We are here using Association Rule to identify the relationship between different products and Classification Rule on the consumers to distinguish them on the basis of pre-defined parameters. The most important information discovered from the combination of these two algorithms supports the retailers in decision making and hence increase their sales.

Market Basket Analysis (MBA) is a widely used technique among marketers to identify the best possible combination of products or services frequently bought by customers. Market Basket Analysis is one of the data mining techniques used in recent times to know the correlation between one items to another purchased. The problem of determining customers preference in terms of items purchased was focused on the traditional and heuristics algorithms with limited factors in the past. However in recent times through this study, building an automated basket analysis system, will help shop owners identify customers purchasing behavior, patterns and identify the relationship between products and item purchased in order to maximize profit through the use of association rule mining. Association rules is one of the data mining techniques which is used for identifying the relationship between one item to another. Association rule is the bedrock of a market basket analysis system as it helps to det...