An Approach for Estimating the Opportunity Cost Using Temporal Association Rule Mining and Clustering

2021 ◽  
pp. 631-643
Author(s):  
Reshu Agarwal
Keyword(s):  
Data Mining ◽  
Inventory Management ◽  
Opportunity Cost ◽  
Temporal Association ◽  
Process Data ◽  
Penalty Cost ◽  
Data Mining Approach ◽  
Emerging Trends ◽  
Temporal Association Rule ◽  
Research Studies

Timely identification of newly emerging trends is needed in business process. Data mining techniques are best suited for the classification, useful patterns extraction and predications which are very important for business support and decision making. Some research studies have also extended the usage of this concept in inventory management to determine opportunity cost based on association rules. Yet, not many research studies have considered the application of the data mining approach on evaluating penalty cost which is also a significant factor to the manager for optimal inventory control. In this paper, two different cases for evaluating penalty cost based on cross-selling effect are presented. An example is illustrated to validate the results.

Temporal Association Rule Mining in Large Databases

Data Mining ◽  
2013 ◽  
pp. 586-602
Author(s):  
A. V. Senthil Kumar ◽  
Adnan Alrabea ◽  
Pedamallu Chandra Sekhar
Keyword(s):  
Data Mining ◽  
Relational Databases ◽  
Weather Forecast ◽  
Temporal Association ◽  
Starting Time ◽  
Sequence Of Events ◽  
Communication Engineering ◽  
Large Databases ◽  
Temporal Association Rule ◽  
Temporal Sequences

Over the last couple of years, data mining technology has been successfully employed to various business domains and scientific areas. One of the main unresolved problems that arise during the data mining process is treating data that contains temporal information. A thorough understanding of this concept requires that the data should be viewed as a sequence of events. Temporal sequences exist extensively in different areas that include economics, finance, communication, engineering, medicine, weather forecast and so on. This chapter proposes a technique that is developed to explore frequent temporal itemsets in the database. The basic idea of this technique is to first partition the database into sub-databases in light of either common starting time or common ending time. Then for each partition, the proposed technique is used progressively to accumulate the number of occurrences of each candidate 2-itemsets. A Directed graph is built using the support of these candidate 2-itemsets (combined from all the sub-databases) as a result of generating all candidate temporal k- itemsets in the database. The above technique may help researchers not only to understand about generating frequent large temporal itemsets but also helps in understanding of finding temporal association rules among transactions within relational databases.

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.

Temporal Association Rule Mining in Large Databases

2010 ◽  
pp. 48-65
Author(s):  
A. V. Senthil Kumar ◽  
Adnan Alrabea ◽  
Pedamallu Chandra Sekhar
Keyword(s):  
Data Mining ◽  
Relational Databases ◽  
Weather Forecast ◽  
Temporal Association ◽  
Starting Time ◽  
Sequence Of Events ◽  
Communication Engineering ◽  
Large Databases ◽  
Temporal Association Rule ◽  
Temporal Sequences

Over the last couple of years, data mining technology has been successfully employed to various business domains and scientific areas. One of the main unresolved problems that arise during the data mining process is treating data that contains temporal information. A thorough understanding of this concept requires that the data should be viewed as a sequence of events. Temporal sequences exist extensively in different areas that include economics, finance, communication, engineering, medicine, weather forecast and so on. This chapter proposes a technique that is developed to explore frequent temporal itemsets in the database. The basic idea of this technique is to first partition the database into sub-databases in light of either common starting time or common ending time. Then for each partition, the proposed technique is used progressively to accumulate the number of occurrences of each candidate 2-itemsets. A Directed graph is built using the support of these candidate 2-itemsets (combined from all the sub-databases) as a result of generating all candidate temporal k- itemsets in the database. The above technique may help researchers not only to understand about generating frequent large temporal itemsets but also helps in understanding of finding temporal association rules among transactions within relational databases.

Temporal Association Rule Mining in Event Sequences

2011 ◽  
pp. 1098-1102
Author(s):  
Sherri K. Harms
Keyword(s):  
Remote Sensing ◽  
Data Mining ◽  
Time Series Data ◽  
Series Data ◽  
Temporal Association ◽  
Temporal Data ◽  
Rule Mining ◽  
Event Sequences ◽  
Use Of Data ◽  
Temporal Association Rule

The emergence of remote sensing, scientific simulation and other survey technologies has dramatically enhanced our capabilities to collect temporal data. However, the explosive growth in data makes the management, analysis, and use of data both difficult and expensive. To meet these challenges, there is an increased use of data mining techniques to index, cluster, classify and mine association rules from time series data (Roddick & Spiliopoulou, 2002; Han, 2001). A major focus of these algorithms is to characterize and predict complex, irregular, or suspicious activity (Han, 2001).

Modified Ranking With Temporal Association Rule Mining in Supply Chains

2020 ◽  
Vol 11 (4) ◽  
pp. 58-71
Author(s):  
Reshu Agarwal
Keyword(s):  
Inventory Management ◽  
Association Rule ◽  
Association Rule Mining ◽  
Temporal Association ◽  
Rule Mining ◽  
Individual Item ◽  
Data Set ◽  
Abc Classification ◽  
Temporal Association Rule ◽  
Time Periods

This article deals with data mining applications for the supply chain inventory management. ABC classification is usually used for inventory items classification because the number of inventory items is so large that it is not computationally feasible to set stock and service control guidelines for each individual item. Moreover, in ABC classification, the inter-relationship between items is not considered. But practically, the sale of one item could affect the sale of other items (cross selling effect). Hence, within time-periods, the inventories should be classified. In this article, a modified approach is proposed considering both time-periods and cross-selling effect to rank inventory items. A numerical example and an empirical study with a data set are used to evaluate the proposed approach. It is illustrated that by using this modified approach, the ranking of items may get affected resulting in higher profit.

IMPLEMENTASI ALGORITMA K-Modes UNTUK MENENTUKAN STRATEGI MARKETING STMIK BUDI DARMA

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Ewin Karman Nduru ◽  
Efori Buulolo ◽  
Pristiwanto Pristiwanto
Keyword(s):  
High School ◽  
Data Mining ◽  
High School Students ◽  
Marketing Strategy ◽  
Process Data ◽  
School Students ◽  
The North ◽  
The Government ◽  
Using Data ◽  
North Sumatra

Universities or institutions that operate in North Sumatra are very many, therefore, of course, competition in accepting new students is very tight, universities or institutions do certain ways or steps to be able to compete with other campuses in gaining interest from community or high school students who will continue their studies to a higher level. STMIK BUDI DARMA Medan (College of Information and Computer Management), is the first computer high school in Medan which was established on March 1, 1996 and received approval from the government through the Minister of Education and Culture, on July 23, 1996 with operating license number 48 / D / O / 1996, in promoting the campus, the team usually formed a promotion team to various regions in the North Sumatra Region to provide information to the community. Students who have learned in this campus are quite a lot who come from various regions in North Sumatra, from this point the need to process data from students who are active in college to be processed using data mining to achieve a target, one method that can be used in data mining, namely the ¬K-Modes clustering (grouping) algorithm. This method is a grouping of student data that will be a help to campus students in promoting, using the K-Modes algorithm is expected to help and become a reference for marketing in determining the marketing strategy STMIK Budi Darma MedanKeywords: STMIK Budi Darma, Marketing Strategy, K-Modes Algorithm.

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