Ordering Policy Using Multi-Level Association Rule Mining

2018 ◽  
Vol 11 (4) ◽  
pp. 84-101 ◽  
Author(s):  
Reshu Agarwal ◽  
Sarla Pareek ◽  
Biswajit Sarkar ◽  
Mandeep Mittal
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Order Quantity ◽  
Rule Mining ◽  
Ordering Policy ◽  
Optimum Order ◽  
Frequent Items ◽  
Multi Level ◽  
Real Scenario ◽  
Frequent Item Sets

In this article, an inventory model for a retailer's ordering policy is studied. Multi-level association rule mining is used to find frequent item-sets at each level by applying different threshold at different levels. During order quantity estimation, category, content, and brand of the items are considered, which leads to the discovery of more specific and concrete knowledge of the required order quantity. At each level, optimum order quantity of frequent items is determined. This assists inventory manager to order optimal quantity of items as per the actual requirement of the item with respect to their category, content and brand. An example is devised to explain the new approach. Further, to understand the effect of above approach in the real scenario, experiments are conducted on the exiting dataset.

Ordering Policy and Inventory Classification Using Temporal Association Rule Mining

2018 ◽  
Vol 6 (1) ◽  
pp. 37-49 ◽  
Author(s):  
Reshu Agarwal
Keyword(s):  
Inventory Management ◽  
Association Rule ◽  
Association Rule Mining ◽  
Temporal Association ◽  
Order Quantity ◽  
Rule Mining ◽  
Ordering Policy ◽  
Temporal Association Rule ◽  
Optimum Order ◽  
Frequent Items

A modified framework that applies temporal association rule mining to inventory management is proposed in this article. The ordering policy of frequent items is determined and inventory is classified based on loss rule. This helps inventory managers to determine optimum order quantity of frequent items together with the most profitable item in each time-span. An example is illustrated to validate the results.

Inventory Classification Using Multi-Level Association Rule Mining

2019 ◽  
Vol 11 (2) ◽  
pp. 1-12 ◽  
Author(s):  
Reshu Agarwal ◽  
Mandeep Mittal
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Levels Of Abstraction ◽  
Frequent Items ◽  
Multi Level ◽  
Multiple Levels ◽  
Real Scenario ◽  
The Impact ◽  
Different Levels

Popular data mining methods support knowledge discovery from patterns that hold in relations. For many applications, it is difficult to find strong associations among data items at low or primitive levels of abstraction. Mining association rules at multiple levels may lead to more informative and refined knowledge from data. Multi-level association rule mining is a variation of association rule mining for finding relationships between items at each level by applying different thresholds at different levels. In this study, an inventory classification policy is provided. At each level, the loss profit of frequent items is determined. The obtained loss profit is used to rank frequent items at each level with respect to their category, content and brand. This helps inventory manager to determine the most profitable item with respect to their category, content and brand. An example is illustrated to validate the results. Further, to comprehend the impact of above approach in the real scenario, experiments are conducted on the exiting dataset.

The Research of Improved Apriori Algorithm

2012 ◽  
Vol 263-266 ◽  
pp. 2179-2184 ◽  
Author(s):  
Zhen Yun Liao ◽  
Xiu Fen Fu ◽  
Ya Guang Wang
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
Frequent Item ◽  
Mining Algorithm ◽  
The Times ◽  
Frequent Item Sets ◽  
Candidate Item ◽  
Improved Algorithm

The first step of the association rule mining algorithm Apriori generate a lot of candidate item sets which are not frequent item sets, and all of these item sets cost a lot of system spending. To solve this problem，this paper presents an improved algorithm based on Apriori algorithm to improve the Apriori pruning step. Using this method, the large number of useless candidate item sets can be reduced effectively and it can also reduce the times of judge whether the item sets are frequent item sets. Experimental results show that the improved algorithm has better efficiency than classic Apriori algorithm.

Association Rule and Quantitative Association Rule Mining among Infrequent Items

2010 ◽  
pp. 15-32 ◽  
Author(s):  
Ling Zhou ◽  
Stephen Yau
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Transactional Databases ◽  
Frequent Items ◽  
Increasing Demand ◽  
Quantitative Association Rule

Association rule mining among frequent items has been extensively studied in data mining research. However, in recent years, there is an increasing demand for mining infrequent items (such as rare but expensive items). Since exploring interesting relationships among infrequent items has not been discussed much in the literature, in this chapter, the authors propose two simple, practical and effective schemes to mine association rules among rare items. Their algorithms can also be applied to frequent items with bounded length. Experiments are performed on the well-known IBM synthetic database. The authors’ schemes compare favorably to Apriori and FP-growth under the situation being evaluated. In addition, they explore quantitative association rule mining in transactional databases among infrequent items by associating quantities of items: some interesting examples are drawn to illustrate the significance of such mining.

Support-Less Association Rule Mining Using Tuple Count Cube

2007 ◽  
Vol 06 (04) ◽  
pp. 271-280
Author(s):  
Qin Ding ◽  
William Perrizo
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Data Cube ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
High Confidence ◽  
Support Level ◽  
Vertical Data ◽  
Frequent Item Sets ◽  
High Support

Association rule mining is one of the important tasks in data mining and knowledge discovery (KDD). The traditional task of association rule mining is to find all the rules with high support and high confidence. In some applications, we are interested in finding high confidence rules even though the support may be low. This type of problem differs from the traditional association rule mining problem; hence, it is called support-less association rule mining. Existing algorithms for association rule mining, such as the Apriori algorithm, cannot be used efficiently for support-less association rule mining since those algorithms mostly rely on identifying frequent item-sets with high support. In this paper, we propose a new model to perform support-less association rule mining, i.e., to derive high confidence rules regardless of their support level. A vertical data structure, the Peano Count Tree (P-tree), is used in our model to represent all the information we need. Based on the P-tree structure, we build a special data cube, called the Tuple Count Cube (T-cube), to derive high confidence rules. Data cube operations, such as roll-up, on T-cube, provide efficient ways to calculate the count information needed for support-less association rule mining.

Optimal Order Quantity and Inventory Classification Using Clustering

2017 ◽  
Vol 4 (2) ◽  
pp. 41-52 ◽  
Author(s):  
Reshu Agarwal
Keyword(s):  
Inventory Management ◽  
Optimal Order ◽  
Order Quantity ◽  
Ordering Policy ◽  
Optimum Order ◽  
Frequent Items ◽  
Clustering Approach ◽  
Optimal Order Quantity ◽  
Data Objects ◽  
Research Studies

Clustering is the process of analyzing data to find clusters of data objects that are similar in some sense to one another. Some research studies have also extended the usage of clustering concept in inventory management. Yet, not many research studies have considered the application of clustering approach on determining both optimal order quantity and loss profit of frequent items. In this paper, ordering policy of frequent items in each cluster is determined and inventory is classified based on loss rule in each cluster. This helps inventory manager to determine optimum order quantity of frequent items together with the most profitable item in each cluster for optimal inventory control. An example is illustrated to validate the results.

Defining the Factors that Effect User Interest on Social Network News Feeds via Fuzzy Association Rule Mining

2013 ◽  
pp. 334-345
Author(s):  
Basar Öztaysi ◽  
Sezi Çevik Onar
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Data Mining Technique ◽  
Data Set ◽  
Frequent Items ◽  
Fuzzy Association Rule ◽  
Fuzzy Association Rule Mining

Social networking became one of the main marketing tools in the recent years since it’s a faster and cheaper way to reach the customers. Companies can use social networks for efficient communication with their current and potential customers but the value created through the usage of social networks depends on how well the organizations use these tools. Therefore a support system which will enhance the usage of these tools is necessary. Fuzzy Association rule mining (FARM) is a commonly used data mining technique which focuses on discovering the frequent items and association rules in a data set and can be a powerful tool for enhancing the usage of social networks. Therefore the aim of the chapter is to propose a fuzzy association rule mining based methodology which will present the potential of using the FARM techniques in the field of social network analysis. In order to reveal the applicability, an experimental evaluation of the proposed methodology in a sports portal will be presented.

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