Proceedings ArticleDOI
EO-ARM: An efficient and optimized k-map based positive-negative association rule mining technique
Chandrasekar Ravi,Neelu Khare +1 more
- pp 1723-1727
TLDR
EO-ARM, an Efficient and Optimized Positive-Negative Association Rule Mining algorithm, which produces both positive as well as negative association rules and optimizes the association rules by introducing a contingency matrix based correlation measure which prunes less interesting rules thereby overcoming the existing limitations.Abstract:
Association Rule Mining is a Data Mining technique which extracts association rules from the given dataset. A good number of research work has been reported in Association Rule Mining algorithms which discovers positive association rules. Amongst them, only a few algorithms have focused on Association Rule Mining algorithms which discovers negative association rules too. Amongst the negative Association Rule Mining algorithms, most of them scans the dataset more than once to identify the frequent item sets and also doesn't guarantee that all the extracted rules are interesting. In order to overcome the above said challenges, EO-ARM, an Efficient and Optimized Positive-Negative Association Rule Mining algorithm has been proposed in this paper. EO-ARM produces both positive as well as negative association rules. It scans the dataset only once (irrespective of the size of dataset) to identify frequent item sets using a two dimensional matrix thereby increasing the efficiency. The two dimensional matrix is conceptually similar to k-map. It also optimizes the association rules by introducing a contingency matrix based correlation measure which prunes less interesting rules thereby overcoming the existing limitations. EO-ARM has been implemented using a binary transaction dataset. Several experiments were performed and an optimal support and confidence threshold has been identified for the given dataset. These optimized support and confidence threshold have been used to find the frequent item sets and generating rules from the dataset. Experimental results also proved that EO-ARM is more efficient in terms of execution time than the standard Apriori algorithm and more optimized in terms of no. of rules generated with the pruning done with the projected correlation measure.read more
Citations
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Journal ArticleDOI
Causation analysis model: Based on AHP and hybrid Apriori-Genetic algorithm
Journal ArticleDOI
An Improved Algorithm for Mining Correlation Item Pairs
An Analysis on Characteristics of Negative Association Rules
Javad Kargar,Fatemeh Hajiloo +1 more
TL;DR: This research analyses the behavior of the negative association rules based on trial and error and emphasizes that extracting only positive rules for covering association rules is not enough.
Book ChapterDOI
Mining Frequent Itemset Using Quine–McCluskey Algorithm
TL;DR: This paper presents an approach which uses Quine–McCluskey algorithm in order to discover frequent itemsets to generate association rules and requires less number of scans compared to other existing techniques.
References
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Proceedings ArticleDOI
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TL;DR: An algorithm is proposed that mines positive and negative association rules without adding any additional measure and extra database scans to find associations among items in a set by mining necessary patterns in a large database.
Journal ArticleDOI
Re-mining item associations: Methodology and a case study in apparel retailing
TL;DR: A new approach to mine price, time and domain related attributes through re-mining of association mining results is proposed, and the underlying factors behind positive and negative relationships can be characterized and described through this second data mining stage.
Proceedings ArticleDOI
Efficient Mining of Generalized Negative Association Rules
TL;DR: This paper proposes a method improved upon the traditional negative association rule mining, which mainly decreases the huge computing cost of mining negative association rules and reduces most non-interesting negative rules.
Proceedings ArticleDOI
Research on Mining Positive and Negative Association Rules Based on Dual Confidence
TL;DR: The experimental result shows that positive and negative association rules mining algorithm can reduce the scale of meaningless association rules, and mine a lot of interestingnegative association rules.
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