Analyzing Price Data to Determine Positive and Negative Product Associations (original) (raw)

Re-mining Positive and Negative Association Mining Results

Positive and negative association mining are well-known and extensively studied data mining techniques to analyze market basket data. Efficient algorithms exist to find both types of association, sepa-rately or imultaneously. Association mining is performed by operating on the transaction data. Despite being an integral part of the transaction data, the pricing and time information has not been incorporated into market basket analysis so far, and additional attributes have been handled using quantitative association mining. In this paper, a new approach is proposed to incorporate price, time and domain related attributes into data mining by re-mining the association mining results. The underlying factors behind positive and negative relationships, as indicated by the association rules, are characterized and described through the second data mining stagere-mining. The applicability of the methodology is demon-strated by analyzing data coming from apparel retailing industry, where price markdown is an essential tool for promoting sales and generating increased revenue.

Improved Positive and Negative Quantitative Association Rule Mining using SAM

Mining and discovering association from huge dataset is one of the common data mining technique, which help to extract interesting knowledge and find dependencies between items in the dataset. Several techniques and algorithms have been proposed to find dependencies between items with positive dependencies and those techniques don’t concentrate on negative dependency calculation. Certain algorithms initiated the findings of negative association rules, even though the techniques are effective, that is only considered the quality in rules. So there is a need of finding both positive and negative quantitative association rules from the dataset. This paper proposes a new technique named as Improved Positive and Negative Quantitative Association Rule Mining using SaM(Split and Merge). It is a new multi-objective based algorithm, which helps to mine a decreased set of positive and negative quantitative association rules rapidly. In addition, this proposal maximizes the following objectives such as improving precision, interestingness, performance and reducing the storage overhead. This also includes the split and merge algorithm for fast data management. in order to obtain set of rules which are interesting, easy to understand, suitable for decision making and provide good coverage of the dataset. The effectiveness of the proposed approach is validated over several real-world datasets.

Mining Negative Association Rules

—Association rule mining is one of the most popular data mining techniques to find associations among items in a set by mining necessary patterns in a large database. Typical association rules consider only items enumerated in transactions. Such rules are referred to as positive association rules. Negative association rules also consider the same items, but in addition consider negated items (i.e. absent from transactions). Negative association rules are useful in market-basket analysis to identify products that conflict with each other or products that complement each other. They are also very useful for constructing associative classifiers. In this paper, we propose an algorithm that mines negative association rules by using conviction measure which does not require extra database scans.

MINING POSITIVE AND NEGATIVE ASSOCIATION RULES: AN APPROACH FOR BINARY TREES

Mining association rules and especially the negative ones has received a lot of attention and has been proved to be useful in the real world. In this work, a set of algorithms for finding both positive and negative association rules (NAR) in databases is presented. A variant of the Apriori, traditional association rules algorithm, is achieved using support and confidence in order to discover two types of NAR; the confined negative association rules (CNR), and the generalized negative association rules (GNAR). For the CNR, where only one negative rule exists among positive ones, the negative rule can be discovered by applying the measure of correlation in terms of the conditional and marginal probability along with the contingency tables. This measure is also used for finding positive rules in the case of branches of itemsets. The negative associations of CNR can be used for substitution of items in market basket analysis. A method of Binary Tree Rules Construction (BTRC) has been developed for the discovery of rules that belong to GNAR , when one or more negative rules along with positive ones exist. In each computation process from disjoint sets, the BTRC produces nested subtrees in order to find the NAR. BTRC is based on successive partitioning of the events of observing a sequence with a certain number of positive and negative items. A set of formulas depending on the height of the tree has been developed. The process can be divided into two parts; the external and the internal subtree process. For the discovery of both types of rules an algorithm (BTA) is developed based on a traditional method and the BTRC.

Re-mining item associations: Methodology and a case study in apparel retailing

2011

Association mining is the conventional data mining technique for analyz-ing market basket data and it reveals the positive and negative associations between items. While being an integral part of transaction data, pricing and time information have not been integrated into market basket analy-sis in earlier studies. This paper proposes a new approach to mine price, time and domain related attributes through re-mining of association mining results. The underlying factors behind positive and negative relationships can be characterized and described through this second data mining stage. The applicability of the methodology is demonstrated through the analy-sis of data coming from a large apparel retail chain, and its algorithmic complexity is analyzed in comparison to the existing techniques.

Applying Negative Association Rule Utilizing Unsupervised Machine Learning Models

Advances in Intelligent Systems and Computing, 2021

Association rules are described as the relationships between objects in the database, and these rules are broadly investigated in the analysis of shopping baskets, to examine the influence of one item on the another in the same transaction. In this paper, a novel technique is suggested that can be the ability to mine thrilling negative association rules between items in the transaction database, by extracting knowledge from that database depending on the purchased quantities. Two clustering techniques are examined in this paper, which are the density-based spatial clustering and K-means of applications with noise (DBSCAN) techniques. The outcomes of these methods are compared to the outcomes of extracting negative association rules without any domain knowledge. The utilization of DBSCAN clustering technique has given better negative association rule mining outcomes of 40,862 rules, with a rate of 0.209% support and 91.840% confidence, when examining on a real-life transaction database. Mining negative association rules depends on the domain knowledge extracted utilizing the K-means clustering technique that has 17,801 rules with a rate of 0.189% support and 85.835% confidence, while mining negative rules without any domain knowledge results in 90,655 rules with rate support of 0.122% and rate confidence of 99.365%, utilizing the same database.

Mining Positive and Negative Fuzzy Association Rules with Item Cost

— While ancient algorithms concern positive associations between binary or quantitative attributes of databases, this paper focuses on mining each positive and negative fuzzy association rules. This work tends to show however, by a deliberate selection of formal logic connectives considerably hyperbolic expressivity is on the market at very little additional value. Ancient algorithms for mining association rules area unit engineered on the binary attributes databases, that as few limitations. Firstly, it cannot concern quantitative attributes; second, solely the positive association rules area unit discovered; third, it treats every item with a similar frequency though completely different item might have different frequency. during this paper, argue a discovery algorithmic rule for mining positive and negative fuzzy association rules to resolve these 3 limitations. Novel approach is given for effectively mining weighted fuzzy association rules (ARs). This paper solve the matter of mining weighted association rules, exploitation associate degree improved model of weighted support and confidence framework for classical and fuzzy positive and negative association rule mining.

Utilizing Apriori Data Mining Techniques on Sales Transactions

Webology, 2022

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

An Empirical Algorithm for High and Low Correlative Association Rule Mining

International Journal of Intelligent Engineering and Systems,, 2018

Support and confidence based Association rules mining algorithms have certain problems. Although other metrics like interest factor, comprehensibility, lift, correlation etc. are available to measure the interestingness of association rules. All the objectives are not suitable for each and every situation. All the objectives which were proposed in the literature, have some drawback, like correlation analysis gives equal importance to the items those are present and absent in transaction database. Resultant the rules generated by this, sometime mislead decision makers. Hence there is a strong need to define some new objectives for association rules that support in effective decision making. In this paper, authors proposed two novel objectives, high correlation and low correlation for 2-variables and 3-variables. These novel objectives clearly indicate that how much or how less two/three items are correlated. On the basis of this, decision makers can form their business strategies. An empirical algorithm for high and low correlative association rules generation is also proposed. With numeric evolution and experiments on the real-life data set, effectiveness has been measured and found that proposed algorithm gives better results.

Determination of Sales Data Patterns Using the Association Rules Apriori Method

2020

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