An alternative approach to mine association rules

Kumar, Ch. Sandeep; Srinivas, K.; Kishor, Peddi; Bhaskar, T.

doi:10.1109/icectech.2011.5942129

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…The idea was that, all item sets cannot be expected to behave similarly and hence it would be more appropriate to evaluate the behavior of different item sets using different minimum support values. 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. In [19,20], methods were published to mine negative association rule by minimizing the number of scans of the dataset.…”

Section: Related Workmentioning

confidence: 99%

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

Ravi

Khare

2014

2014 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2014]

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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.

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Section: Related Workmentioning

confidence: 99%

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

Ravi

Khare

2014

2014 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2014]

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Mining Positive and Negative Association Rule from Frequent and Infrequent Pattern based on IMLMS_GA

Rai¹,

Jain²,

Jain³

2013

IJCA

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Association rule mining is one of the most significant tasks in data mining. The essential concept of association rule is to mine the positive patterns from transaction database. But mining the negative patterns has also received the interest of publishers in this region. This paper shows an efficient algorithm (IMLMS-GA) for mining both positive and negative association rules in transaction databases. The goal of this study is to build up a new model for mining negative and positive (PR & NR) association rules out of transaction data sets. The proposed model is based on two models, the MLMS model and the Interesting Multiple Level Minimum Supports (IMLMS) model. This paper proposes a new approach (IMLMS-GA) for mining both negative and positive association rules. The interesting frequent patterns and infrequent patterns mined by the IMLMS-GA algorithm. This algorithm is accomplished in two phase: a. First phase find all frequent patterns & infrequent patterns b. Second phase efficiently generate positive and negative association rule by using useful frequent pattern set. The experimental results prove that the IMLMS-GA can remove the scale of uninteresting association rules and generates better results than the previous positive and negative association rule mining algorithm.

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An alternative approach to mine association rules

Cited by 2 publications

References 9 publications

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

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

Mining Positive and Negative Association Rule from Frequent and Infrequent Pattern based on IMLMS_GA

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