Loss profit estimation using association rule mining with clustering
Data mining is the technique to find hidden patterns from a very large volume of historical data. Association rule is a type of data mining that correlates one set of items or events with another set of items or events. Another data mining strategy is clustering technique. This technique is used to...
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Growing Science
2015-02-01
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Online Access: | http://www.growingscience.com/msl/Vol5/msl_2015_4.pdf |
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doaj-5c7c8767f3ec4fae997160756d3cb0562020-11-25T00:00:51ZengGrowing ScienceManagement Science Letters1923-29341923-93432015-02-015216717410.5267/j.msl.2015.1.004Loss profit estimation using association rule mining with clusteringMandeep MittalSarla Pareek Reshu Agarwal Data mining is the technique to find hidden patterns from a very large volume of historical data. Association rule is a type of data mining that correlates one set of items or events with another set of items or events. Another data mining strategy is clustering technique. This technique is used to create partitions so that all members of each set are similar according to a specified set of metrics. Both the association rule mining and clustering helps in more effective individual and group decision making for optimal inventory control. Owing to the above facts, association rules are mined from each cluster to find frequent items and then loss profit is calculated for each frequent item. Initially, the clustering algorithm is used to partition the transactional database into different clusters. Apriori, a classic data mining algorithm is utilized for mining association rules from each cluster to find frequent items. Later the loss profit is calculated for each frequent item. The obtained loss profit is used to rank frequent items in each cluster. Thus, the ranking of frequent items in each cluster using the proposed approach greatly facilitate optimal inventory control. An example is illustrated to validate the results.http://www.growingscience.com/msl/Vol5/msl_2015_4.pdfData miningAssociation rule miningClusteringInventory controlLoss ProfitApriori algorithm |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Mandeep Mittal Sarla Pareek Reshu Agarwal |
spellingShingle |
Mandeep Mittal Sarla Pareek Reshu Agarwal Loss profit estimation using association rule mining with clustering Management Science Letters Data mining Association rule mining Clustering Inventory control Loss Profit Apriori algorithm |
author_facet |
Mandeep Mittal Sarla Pareek Reshu Agarwal |
author_sort |
Mandeep Mittal |
title |
Loss profit estimation using association rule mining with clustering |
title_short |
Loss profit estimation using association rule mining with clustering |
title_full |
Loss profit estimation using association rule mining with clustering |
title_fullStr |
Loss profit estimation using association rule mining with clustering |
title_full_unstemmed |
Loss profit estimation using association rule mining with clustering |
title_sort |
loss profit estimation using association rule mining with clustering |
publisher |
Growing Science |
series |
Management Science Letters |
issn |
1923-2934 1923-9343 |
publishDate |
2015-02-01 |
description |
Data mining is the technique to find hidden patterns from a very large volume of historical data. Association rule is a type of data mining that correlates one set of items or events with another set of items or events. Another data mining strategy is clustering technique. This technique is used to create partitions so that all members of each set are similar according to a specified set of metrics. Both the association rule mining and clustering helps in more effective individual and group decision making for optimal inventory control. Owing to the above facts, association rules are mined from each cluster to find frequent items and then loss profit is calculated for each frequent item. Initially, the clustering algorithm is used to partition the transactional database into different clusters. Apriori, a classic data mining algorithm is utilized for mining association rules from each cluster to find frequent items. Later the loss profit is calculated for each frequent item. The obtained loss profit is used to rank frequent items in each cluster. Thus, the ranking of frequent items in each cluster using the proposed approach greatly facilitate optimal inventory control. An example is illustrated to validate the results. |
topic |
Data mining Association rule mining Clustering Inventory control Loss Profit Apriori algorithm |
url |
http://www.growingscience.com/msl/Vol5/msl_2015_4.pdf |
work_keys_str_mv |
AT mandeepmittal lossprofitestimationusingassociationruleminingwithclustering AT sarlapareek lossprofitestimationusingassociationruleminingwithclustering AT reshuagarwal lossprofitestimationusingassociationruleminingwithclustering |
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1725443067511570432 |