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Proceedings ArticleDOI

EO-ARM: An efficient and optimized k-map based positive-negative association rule mining technique

20 Mar 2014-pp 1723-1727

TL;DR: 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.
Citations
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DOI
Javad Kargar, Fatemeh Hajiloo1Institutions (1)
01 Jun 2019-
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.
Abstract: Association rules are one of the data and web mining techniques which aim to discover the frequent patterns among itemsets in a transactional database. Frequent patterns and correlation between itemsets in datasets and databases are extracted by these interesting rules. The association rules are positive or negative, and each has its own specific characteristics and definitions. The mentioned algorithms of the discovery of association rules are always facing challenges, including the extraction of only positive rules, while negative rules in databases are also important for a manager’s decision making. Also, the threshold level for support and confidence criteria is always manual with trial and error by the user and the proper place or the characteristics of datasets is not clear for these rules. This research analyses the behavior of the negative association rules based on trial and error. After analyzing the available algorithms, the most efficient algorithm is implemented and then the negative rules are extracted. This test repeats on several standard datasets to evaluate the behavior of the negative rules. The analyses of the achieved outputs reveal that some of the interesting patterns are detected by the negative rules, while the positive rules could not detect such helpful rules. This study emphasizes that extracting only positive rules for covering association rules is not enough.

1 citations


Cites methods from "EO-ARM: An efficient and optimized ..."

  • ...2014 [16, 17] K-map Proper precision for less items Item Limit, Complexity of karnaugh map Binary conversion and accuracy chart...

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Book ChapterDOI
Kanishka Bajpayee1, Surya Kant2, Bhaskar Pant3, Ankur Chaudhary3  +1 moreInstitutions (3)
01 Jan 2016-
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.
Abstract: This paper presents an approach which uses Quine–McCluskey algorithm in order to discover frequent itemsets to generate association rules. In this approach, the given transaction database is converted into a Boolean matrix form to discover frequent itemsets. After generating the Boolean matrix of given database Quine–McCluskey algorithm is applied. Quine–McCluskey algorithm minimizes the given Boolean matrix to generate the frequent itemset pattern. This method requires less number of scans compared to other existing techniques.

References
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Proceedings ArticleDOI
01 Jun 1993-
TL;DR: An efficient algorithm is presented that generates all significant association rules between items in the database of customer transactions and incorporates buffer management and novel estimation and pruning techniques.
Abstract: We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an efficient algorithm that generates all significant association rules between items in the database. The algorithm incorporates buffer management and novel estimation and pruning techniques. We also present results of applying this algorithm to sales data obtained from a large retailing company, which shows the effectiveness of the algorithm.

15,011 citations


Proceedings Article
01 Jul 1998-
TL;DR: Two new algorithms for solving thii problem that are fundamentally different from the known algorithms are presented and empirical evaluation shows that these algorithms outperform theknown algorithms by factors ranging from three for small problems to more than an order of magnitude for large problems.
Abstract: We consider the problem of discovering association rules between items in a large database of sales transactions. We present two new algorithms for solving thii problem that are fundamentally different from the known algorithms. Empirical evaluation shows that these algorithms outperform the known algorithms by factors ranging from three for small problems to more than an order of magnitude for large problems. We also show how the best features of the two proposed algorithms can be combined into a hybrid algorithm, called AprioriHybrid. Scale-up experiments show that AprioriHybrid scales linearly with the number of transactions. AprioriHybrid also has excellent scale-up properties with respect to the transaction size and the number of items in the database.

10,858 citations



Journal ArticleDOI
TL;DR: This paper proposes MOPNAR, a new multiobjective evolutionary algorithm, in order to mine a reduced set of positive and negative quantitative association rules with low computational cost and maximizes three objectives-comprehensibility, interestingness, and performance-in order to obtain rules that are interesting, easy to understand, and provide good coverage of the dataset.
Abstract: Most of the algorithms for mining quantitative association rules focus on positive dependencies without paying particular attention to negative dependencies. The latter may be worth taking into account, however, as they relate the presence of certain items to the absence of others. The algorithms used to extract such rules usually consider only one evaluation criterion in measuring the quality of generated rules. Recently, some researchers have framed the process of extracting association rules as a multiobjective problem, allowing us to jointly optimize several measures that can present different degrees of trade-off depending on the dataset used. In this paper, we propose MOPNAR, a new multiobjective evolutionary algorithm, in order to mine a reduced set of positive and negative quantitative association rules with low computational cost. To accomplish this, our proposal extends a recent multiobjective evolutionary algorithm based on decomposition to perform an evolutionary learning of the intervals of the attributes and a condition selection for each rule, while introducing an external population and a restarting process to store all the nondominated rules found and to improve the diversity of the rule set obtained. Moreover, this proposal maximizes three objectives-comprehensibility, interestingness, and performance-in order to obtain rules that are interesting, easy to understand, and provide good coverage of the dataset. The effectiveness of the proposed approach is validated over several real-world datasets.

90 citations


"EO-ARM: An efficient and optimized ..." refers methods in this paper

  • ...In [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18], methods were published for eliminating the less interesting rules which is one of the most challenging task in mining negative association rules....

    [...]


Journal ArticleDOI
TL;DR: An approach to spatial association rule mining from datasets projected at a temporal bar in which the contextual situation is considered while generating positive and negative frequent itemsets and the numerical evaluation shows that the algorithm is more efficient at generating specific, reliable and robust information than traditional algorithms.
Abstract: This paper proposes a new approach to mine context based positive and negative spatial association rules as they might be applied to hydrocarbon prospection. Many researchers are currently using an Apriori algorithm on spatial databases but this algorithm does not utilize the strengths of positive and negative association rules and of time series analysis, hence it misses the discovery of very interesting and useful associations present in the data. In dense spatial databases, the number of negative association rules is much higher compared to the positive rules which need exploitation. Using positive and negative association rule discovery and then pruning out the uninteresting rules consumes resources without much improvement in the overall accuracy of the knowledge discovery process. The associations among different objects and lattices are strongly dependent upon the context, particularly where context is the state of entity, environment or action. We propose an approach to spatial association rule mining from datasets projected at a temporal bar in which the contextual situation is considered while generating positive and negative frequent itemsets. An extended algorithm based on the Apriori approach is developed and compared with existing spatial association rule algorithms. The algorithm for positive and negative association rule mining is based on Apriori algorithm which is further extended to include context variable and simulate temporal series spatial inputs. The numerical evaluation shows that our algorithm is more efficient at generating specific, reliable and robust information than traditional algorithms.

48 citations


"EO-ARM: An efficient and optimized ..." refers methods in this paper

  • ...In [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18], methods were published for eliminating the less interesting rules which is one of the most challenging task in mining negative association rules....

    [...]


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