Loss Profit Estimation Using Temporal Association Rule Mining

Loss Profit Estimation Using Temporal Association Rule Mining

Reshu Agarwal (Apaji Institute of Mathematics and Applied Computer Technology, Banasthali University, Rajasthan, India), Mandeep Mittal (Department of Computer Science Engineering, Amity School of Engineering and Technology, Bijwasan, New Delhi, India) and Sarla Pareek (Apaji Institute of Mathematics and Applied Computer Technology, Banasthali University, Rajasthan, India)
Copyright: © 2016 |Pages: 13
DOI: 10.4018/IJBAN.2016010103
OnDemand PDF Download:
$30.00
List Price: $37.50

Abstract

Temporal association rule mining is a data mining technique in which relationships between items which satisfy certain timing constraints can be discovered. This paper presents the concept of temporal association rules in order to solve the problem of classification of inventories by including time expressions into association rules. Firstly, loss profit of frequent items is calculated by using temporal association rule mining algorithm. Then, the frequent items in particular time-periods are ranked according to descending order of loss profits. The manager can easily recognize most profitable items with the help of ranking found in the paper. An example is illustrated to validate the results.
Article Preview

Introduction

Data mining is the process of finding hidden, non-trivial and previously unknown information in large collection of data (Sahu, Shrma, & Gondhalakar, 2011). The tasks of data mining are very diverse and distinct because there are many patterns in a large data base. Different kinds of methods and techniques, like clustering, association rule mining, temporal association rule mining, and classification can be used to find different kinds of patterns (Zhao & Bhowmick, 2003).

Further, (Mannila & Raiha, 1987) have done work for determining functional dependencies in database theory. However, association rule mining received a great attention (Agrawal, Imielinski, & Swami, 1993). It is used to find fascinating rules from large collections of data which expresses an association or relation between items or sets of items. The most important algorithm for generating association rules is apriori-algorithm. This algorithm is designed to operate on databases containing transactions. It works to find the items for frequent item-set based on minimum support and generate association rules based on threshold confidence. Further, many variants of mining association rules are studied to explore more mining capabilities, such as incremental updating (Lee, Lin, & Chen, 2001), mining of generalized and multi-level rules (Srikant & Agrawal, 1995), mining of multi-dimensional rules (Ng & Han, 1994) and temporal association rule discovery (Ale & Rossi, 2000). Temporal rules are important class of methods of finding patterns in data at particular time periods. Temporal association rules is an interesting extension to apriroi-algorithm by including a temporal dimension, it leads to different forms of association rules such as discovering association rules that may hold during some time intervals (Li, Ning, Wang, & Jajodia, 2001). However, they did not consider the individual exhibition period of each item. The exhibition period of an item is the time duration from the partition when this item appears in the transaction database to the partition when this item no longer exists (Huang, Dai, & Chen, 2007). As a result, the concept of general temporal association rules has been proposed where the items are allowed to have different exhibition periods and their supports are made in accordance with their exhibition periods (Lee, Chen, & Lin, 2003).

Further, inventory management is mainly about identifying the amount and the position of the goods that a firm has in their inventory. It is imperative as it helps to defend the intended course of production against the chance of running out of important materials or goods. Every organization constantly strives to maintain optimum inventory to be able to meet its requirements and avoid over or under inventory that can impact the financial figures. For inventory management, many researchers have devoted a great amount of efforts in developing inventory models viz. Porteus, 1986; Rosenblatt & Lee, 1986; Schwaller, 1988. These models were extended by Salameh & Jaber (2000) for imperfect quality items. Jaggi, Goel, & Mittal (2011, 2013) formulated an inventory model for deteriorating items with imperfect quality. Jaggi & Mittal (2011, 2012) developed an inventory model with joint effect of inspection, deterioration, time-dependent demand, inflation and time value of money. The management of inventory can become more effective, if inventory is classified into categories based on some criteria like ABC classification, loss profit, and cross-selling effect. Mittal, Pareek, & Agarwal (2014) extended economic order quantity model considering time expressions into association rules. The management of inventory can become more effective, if inventory is classified into categories based on some criteria like ABC classification, loss profit, and cross-selling effect. In ABC classification, firstly the annual dollar usage of item is determined and ranked in descending order starting with the largest value of dollar usage. Secondly, the ranked list is divided into three groups: A (most important), B (intermediate in importance), and C (least importance). The detailed explanation of the ABC classification can be found from Chase, Aquilano, & Jacobs (1998), and Silver, Pyke, & Peterson (1998).

Complete Article List

Search this Journal:
Reset
Open Access Articles: Forthcoming
Volume 5: 4 Issues (2018): 1 Released, 3 Forthcoming
Volume 4: 4 Issues (2017)
Volume 3: 4 Issues (2016)
Volume 2: 4 Issues (2015)
Volume 1: 4 Issues (2014)
View Complete Journal Contents Listing